Jordan Boyd-Graber: Pubs (Project)

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ASED

Denis Peskov, Benny Cheng, Ahmed Elgohary Ghoneim, Joe Barrow, Cristian Danescu-Niculescu-Mizil, and Jordan Boyd-Graber. It Takes Two to Lie: One to Lie and One to Listen. Association for Computational Linguistics, 2020. [Video] [Podcast] [Data and Code] [Bibtex]
```
@inproceedings{Peskov:Cheng:Elgohary:Barrow:Danescu-Niculescu-Mizil:Boyd-Graber-2020,
	Title = {It Takes Two to Lie: One to Lie and One to Listen},
	Author = {Denis Peskov and Benny Cheng and Ahmed Elgohary and Joe Barrow and Cristian Danescu-Niculescu-Mizil and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2020},
	Location = {The Cyberverse Simulacrum of Seattle},
	Url = {http://cs.umd.edu/~jbg//docs/2020_acl_diplomacy.pdf},
}
```
Accessible Abstract: Machine learning techniques to detect deception in online communications requires training and evaluation data. However, there is a dearth of data either because of uncertain gold labels or privacy concerns; we create a new, large deception-centered dataset in the online game of Diplomacy. We gathered 17,289 messages from 12 games (each of which took over a month) involving 84 players, the majority of which were unique users. This data was collected with a custom-made bot that allowed us to collect messages and annotations. The user pool was created from scratch: we varied participant demographics across gender, age, nationality, and past game experience. Some of our participants included the former president of the Diplomacy players' association, several top ranked players in the world, a board game shop owner, and scientists. We create machine learning models to detect lies using linguistic, context, and power-dynamic features. Our best model had similar lie detection accuracy to humans.

Adaptive Heads-up Displays for Simultaneous Interpretation

Alvin Grissom II, Jo Shoemaker, Benjamin Goldman, Ruikang Shi, Craig Stewart, C. Anton Rytting, Leah Findlater, Jordan Boyd-Graber, Wenyan Li, Alvin Grissom II, and Jordan Boyd-Graber. Rapidly Piloting Real-time Linguistic Assistance for Simultaneous Interpreters with Untrained Bilingual Surrogates. Linguistic Resources and Evaluation Conference, 2024. [Bibtex]

@inproceedings{Grissom-II:Shoemaker:Goldman:Shi:Stewart:Rytting:Findlater:Boyd-Graber:Li:Grissom-II:Boyd-Graber-2024,
	Author = {Alvin {Grissom II} and Jo Shoemaker and Benjamin Goldman and Ruikang Shi and Craig Stewart and C. Anton Rytting and Leah Findlater and Jordan Boyd-Graber},
	Location = {Torino, Italy},
	Url = {http://cs.umd.edu/~jbg//docs/2024_lrec_siminthelp.pdf},
	Booktitle = {Linguistic Resources and Evaluation Conference},
	Year = {2024},
	Title = {Rapidly Piloting Real-time Linguistic Assistance for Simultaneous Interpreters with Untrained Bilingual Surrogates},
}

Chen Zhao, Chenyan Xiong, Xin Qian, and Jordan Boyd-Graber. Complex Factoid Question Answering with a Free-Text Knowledge Graph. ACM International Conference on World Wide Web, 2020. [Video] [Bibtex]

@inproceedings{Zhao:Xiong:Qian:Boyd-Graber-2020,
	Title = {Complex Factoid Question Answering with a Free-Text Knowledge Graph},
	Author = {Chen Zhao and Chenyan Xiong and Xin Qian and Jordan Boyd-Graber},
	Booktitle = {ACM International Conference on World Wide Web},
	Year = {2020},
	Location = {Taipei, Taiwan},
	Url = {http://cs.umd.edu/~jbg//docs/2020_www_delft.pdf},
}

Wenyan Li, Alvin Grissom II, and Jordan Boyd-Graber. An Attentive Recurrent Model for Incremental Prediction of Sentence-final Verbs. Findings of EMNLP, 2020. [Bibtex]

@article{Li:Grissom-II:Boyd-Graber-2020,
	Title = {An Attentive Recurrent Model for Incremental Prediction of Sentence-final Verbs},
	Author = {Wenyan Li and Alvin {Grissom II} and Jordan Boyd-Graber},
	Journal = {Findings of EMNLP},
	Year = {2020},
	Url = {http://cs.umd.edu/~jbg//docs/2020_findings_verbs.pdf},
}

Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, and Jordan Boyd-Graber. Mitigating Noisy Inputs for Question Answering. Conference of the International Speech Communication Association, 2019. [Bibtex]

@inproceedings{Peskov:Barrow:Rodriguez:Neubig:Boyd-Graber-2019,
	Title = {Mitigating Noisy Inputs for Question Answering},
	Author = {Denis Peskov and Joe Barrow and Pedro Rodriguez and Graham Neubig and Jordan Boyd-Graber},
	Booktitle = {Conference of the International Speech Communication Association},
	Year = {2019},
	Location = {Graz, Austria},
	Url = {http://cs.umd.edu/~jbg//docs/2019_interspeech_asr},
}

Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, and Graham Neubig. Automatic Estimation of Simultaneous Interpreter Performance. Association for Computational Linguistics, 2018. [Bibtex]

@inproceedings{Stewart:Vogler:Hu:Boyd-Graber:Neubig-2018,
	Title = {Automatic Estimation of Simultaneous Interpreter Performance},
	Author = {Craig Stewart and Nikolai Vogler and Junjie Hu and Jordan Boyd-Graber and Graham Neubig},
	Booktitle = {Association for Computational Linguistics},
	Year = {2018},
	Location = {Melbourne, Australia},
	Url = {http://cs.umd.edu/~jbg//docs/2018_acl_interpeval.pdf},
}

Adobe

Ishani Mondal, Zongxia Li, Yufang Hou, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Boyd-Graber. SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement. Findings of the Empirical Methods in Natural Language Processing, 2024. [Bibtex]

@article{Mondal:Li:Hou:Natarajan:Garimella:Bandyopadhyay:Boyd-Graber-2024,
	Title = {SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement},
	Author = {Ishani Mondal and Zongxia Li and Yufang Hou and Anandhavelu Natarajan and Aparna Garimella and Sambaran Bandyopadhyay and Jordan Boyd-Graber},
	Year = {2024},
	Journal = {Findings of the Empirical Methods in Natural Language Processing},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_diagramgen.pdf},
}

Adobe

Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, and Jordan Boyd-Graber. A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. Empirical Methods in Natural Language Processing, 2024. [Code and Data] [Research Talk] [Bibtex]
```
@inproceedings{Balepur:Shu:Hoyle:Robey:Feng:Goldfarb-Tarrant:Boyd-Graber-2024,
	Title = {A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick},
	Author = {Nishant Balepur and Matthew Shu and Alexander Hoyle and Alison Robey and Shi Feng and Seraphina Goldfarb-Tarrant and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2024},
	Location = {Miami},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_mnemonic.pdf},
}
```
Accessible Abstract: Learning vocabulary (e.g., benevolent) can be tedious, but using mnemonics (e.g., benevolent sounds like "benefits," and a kind boss gives benefits) makes it more engaging and effective. This paper introduces SMART, a large language model trained to produce mnemonics based on feedback from flashcard learners. Students struggle to predict which mnemonics will help them most. Still, by training SMART on both student preferences and learning outcomes, we can generate mnemonics as effectively as GPT-4, but at a much lower cost.

Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, and Jordan Boyd-Graber. Adapting Coreference Resolution Models through Active Learning. Association for Computational Linguistics, 2022. [Code] [Bibtex]

@inproceedings{Yuan:Xia:May:Durme:Boyd-Graber-2022,
	Author = {Michelle Yuan and Patrick Xia and Chandler May and Benjamin Van Durme and Jordan Boyd-Graber},
	Title = {Adapting Coreference Resolution Models through Active Learning},
	Booktitle = {Association for Computational Linguistics},
	Year = {2022},
	Location = {Dublin},
	Url = {http://cs.umd.edu/~jbg//docs/2022_acl_alcoref.pdf},
}

Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, and Jordan Boyd-Graber. Automatic Song Translation for Tonal Languages. Findings of the Association for Computational Linguistics, 2022. [Translation Examples (with sound)] [Code] [Bibtex]

@article{Guo:Zhang:Zhang:He:Zhang:Xie:Boyd-Graber-2022,
	Author = {Fenfei Guo and Chen Zhang and Zhirui Zhang and Qixin He and Kejun Zhang and Jun Xie and Jordan Boyd-Graber},
	Title = {Automatic Song Translation for Tonal Languages},
	Journal = {Findings of the Association for Computational Linguistics},
	Year = {2022},
	Location = {Dublin},
	Url = {http://cs.umd.edu/~jbg//docs/2022_acl_ast.pdf},
}

BETTER

Yoshinari Fujinuma, Jordan Boyd-Graber, and Katharina Kann. How Does Multilingual Pretraining Affect Cross-Lingual Transferability?. Association for Computational Linguistics, 2022. [Code] [Bibtex]

@inproceedings{Fujinuma:Boyd-Graber:Kann-2022,
	Author = {Yoshinari Fujinuma and Jordan Boyd-Graber and Katharina Kann},
	Title = {How Does Multilingual Pretraining Affect Cross-Lingual Transferability?},
	Booktitle = {Association for Computational Linguistics},
	Year = {2022},
	Location = {Dublin},
	Url = {http://cs.umd.edu/~jbg//docs/2022_acl_multilingbert.pdf},
}

Wanrong He, Andrew Mao, and Jordan Boyd-Graber. Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain QA. Findings of Empirical Methods in Natural Language Processing, 2022. [Code] [Data] [Research Talk] [Bibtex]
```
@article{He:Mao:Boyd-Graber-2022,
	Title = {Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain QA},
	Author = {Wanrong He and Andrew Mao and Jordan Boyd-Graber},
	Journal = {Findings of Empirical Methods in Natural Language Processing},
	Year = {2022},
	Location = {Abu Dhabi},
	Url = {http://cs.umd.edu/~jbg//docs/2022_emnlp_cheaters.pdf},
}
```
Accessible Abstract: When the Covid pandemic it, trivia games moved online. With it came cheating: people tried to quickly Google answers. This is bad for sportsmanship, but a good source of training data for helping teach computers how to find answers. We built an interface to harvest this training data from trivia players, fed these into retrieval-based QA systems, showing that these queries were better than the automatically generated queries used by the current state of the art.
Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul, and Jordan Boyd-Graber. Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries. Association for Computational Linguistics, 2020. [Preprint] [Video] [Code] [Bibtex]
```
@inproceedings{Zhang:Fujinuma:Paul:Boyd-Graber-2020,
	Author = {Mozhi Zhang and Yoshinari Fujinuma and Michael J. Paul and Jordan Boyd-Graber},
	Title = {Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries},
	Booktitle = {Association for Computational Linguistics},
	Year = {2020},
	Location = {The Cyberverse Simulacrum of Seattle},
	Url = {http://cs.umd.edu/~jbg//docs/2020_acl_refine.pdf},
}
```
Accessible Abstract: Computers need to represent words in a computer-readable way. This work talks about how slightly moving these representations for words in different languages to be closer to a small list of translations (like from a dictionary) after doing fancy machine learning works better on downstream tasks (e.g., guessing grammatical category of a word) but hurts on asking the algorithm for translations of unseen words.
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. Cold-start Active Learning through Self-Supervised Language Modeling. Empirical Methods in Natural Language Processing, 2020. [Video] [Code] [Bibtex]
```
@inproceedings{Yuan:Lin:Boyd-Graber-2020,
	Title = {Cold-start Active Learning through Self-Supervised Language Modeling},
	Author = {Michelle Yuan and Hsuan-Tien Lin and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2020},
	Location = {The Cyberverse Simulacrum of Punta Cana, Dominican Republic},
	Url = {http://cs.umd.edu/~jbg//docs/2020_emnlp_alps.pdf},
}
```
Accessible Abstract: Labeling data is a fundamental bottleneck in machine learning, especially for NLP, due to annotation cost and time. For medical text, obtaining labeled data is challenging because of privacy issues or shortage in expertise. Thus, active learning can be employed to recognize the most relevant examples and then query labels from an oracle. However, developing a strategy for selecting examples to label is non-trivial. Active learning is difficult to use in cold-start; all examples confuse the model because it has not trained on enough data. Fortunately, modern NLP provides an additional source of information: pre-trained language models. In our paper, we propose an active learning strategy called ALPS to find sentences that perplex the language model. We evaluate our approach on sentence classification datasets spanning across different domains. Results show that ALPS is an efficient active learning strategy that is competitive with state-of-the-art approaches.
Michelle Yuan, Mozhi Zhang, Benjamin Van Durme, Leah Findlater, and Jordan Boyd-Graber. Interactive Refinement of Cross-Lingual Word Embeddings. Empirical Methods in Natural Language Processing, 2020. [Audio of Paper Readthrough] [Video of Paper Readthrough] [Code] [Conference Talk] [Bibtex]
```
@inproceedings{Yuan:Zhang:Van-Durme:Findlater:Boyd-Graber-2020,
	Title = {Interactive Refinement of Cross-Lingual Word Embeddings},
	Author = {Michelle Yuan and Mozhi Zhang and Benjamin {Van Durme} and Leah Findlater and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2020},
	Location = {The Cyberverse Simulacrum of Punta Cana, Dominican Republic},
	Url = {http://cs.umd.edu/~jbg//docs/2020_emnlp_clime.pdf},
}
```
Accessible Abstract: Language technologies sometimes need to be quickly deployed in low-resource languages. For example, in the 2010 Haiti earthquake, researchers used machine learning models to analyze social media and text messages to gain situational awareness. We introduce CLIME, an interactive system that can help in these scenarios: users see which words related to the task the system thinks are similar, corrects the model to push similar words together and dissimilar words apart.

CAREER

Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. [Webpage] [Bibtex]

@article{Rodriguez:Feng:Iyyer:He:Boyd-Graber-Preprint,
	Title = {Quizbowl: The Case for Incremental Question Answering},
	Author = {Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan Boyd-Graber},
	Journal = {ArXiv},
	Year = {Preprint},
	Url = {https://arxiv.org/abs/1904.04792},
}

Shi Feng and Jordan Boyd-Graber. Learning to Explain Selectively: A Case Study on Question Answering. Empirical Methods in Natural Language Processing, 2022. [Research Teaser] [Code and Data] [Bibtex]
```
@inproceedings{Feng:Boyd-Graber-2022,
	Title = {Learning to Explain Selectively: A Case Study on Question Answering},
	Author = {Shi Feng and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2022},
	Location = {Abu Dhabi},
	Url = {http://cs.umd.edu/~jbg//docs/2022_emnlp_augment.pdf},
}
```
Accessible Abstract: Many AI methods are a black box: input goes in, predictions come out. While there are many AI explanation tools that you can add to these predictions, how do you know if they are any good. In this work presented at EMNLP, if you put a human in front of a AI that's trying to answer questions, our hypothesis is that you can measure how good the underlying explanations are by how much the human's score goes up. This 2022 EMNLP publication not just measures which combinations of explanations are most effective for an individual. We use bandit exploration to quickly figure out what set of explanations best help a specific user.
HyoJung Han, Marine Carpuat, and Jordan Boyd-Graber. SimQA: Detecting Simultaneous MT Errors through Word-by-Word Question Answering. Empirical Methods in Natural Language Processing, 2022. [Code] [Research Talk] [Bibtex]
```
@inproceedings{Han:Carpuat:Boyd-Graber-2022,
	Title = {SimQA: Detecting Simultaneous MT Errors through Word-by-Word Question Answering},
	Author = {HyoJung Han and Marine Carpuat and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2022},
	Location = {Abu Dhabi},
	Url = {http://cs.umd.edu/~jbg//docs/2022_emnlp_simqa.pdf},
}
```
Accessible Abstract: Simultaneous interpretation (where a translation happens word by word before the source sentence is finished) is difficult to evaluate. We created a new evaluation framework based on the following scenario: imagine that you're thrown into a trivia gameshow where you don't know the language. Specifically, it's a game format where you interrupt the question word by word as soon as possible. Our hypothesis is that a monolingual player (who doesn't speak the source language) will be able to do better in the game with a better simultaneous translation system. In this 2022 EMNLP publication, we show that this evaluation is not only cheaper (you just need to translate the answer) but can also detect hallucinations and undertranslations better than existing evaluation methods.
Wanrong He, Andrew Mao, and Jordan Boyd-Graber. Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain QA. Findings of Empirical Methods in Natural Language Processing, 2022. [Code] [Data] [Research Talk] [Bibtex]
```
@article{He:Mao:Boyd-Graber-2022,
	Title = {Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain QA},
	Author = {Wanrong He and Andrew Mao and Jordan Boyd-Graber},
	Journal = {Findings of Empirical Methods in Natural Language Processing},
	Year = {2022},
	Location = {Abu Dhabi},
	Url = {http://cs.umd.edu/~jbg//docs/2022_emnlp_cheaters.pdf},
}
```
Accessible Abstract: When the Covid pandemic it, trivia games moved online. With it came cheating: people tried to quickly Google answers. This is bad for sportsmanship, but a good source of training data for helping teach computers how to find answers. We built an interface to harvest this training data from trivia players, fed these into retrieval-based QA systems, showing that these queries were better than the automatically generated queries used by the current state of the art.
Chenglei Si, Chen Zhao, Sewon Min, and Jordan Boyd-Graber. Re-Examining Calibration: The Case of Question Answering. Findings of Empirical Methods in Natural Language Processing, 2022. [Code] [Research Talk] [Bibtex]
```
@article{Si:Zhao:Min:Boyd-Graber-2022,
	Title = {Re-Examining Calibration: The Case of Question Answering},
	Author = {Chenglei Si and Chen Zhao and Sewon Min and Jordan Boyd-Graber},
	Journal = {Findings of Empirical Methods in Natural Language Processing},
	Year = {2022},
	Location = {Abu Dhabi},
	Url = {http://cs.umd.edu/~jbg//docs/2022_emnlp_calibration.pdf},
}
```
Accessible Abstract: Calibration is an important problem in question answering: if a search engine or virtual assistant doesn't know the answer to a question, you should probably abstain from showing an answer (to save embarassment, as when Google said a horse had six legs). This EMNLP Findings paper shows that existing metrics to test how good a QA calibration push calibrated confidence toward the average confidence. We proposed an alternate method both for evaluation and to generate better calibration by looking how models change as they learn.
Pedro Rodriguez, Joe Barrow, Alexander Hoyle, John P. Lalor, Robin Jia, and Jordan Boyd-Graber. Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?. Association for Computational Linguistics, 2021. [Results and Code] [Paper Read Aloud] [Research Talk Video] [Code and Data] [Bibtex]
```
@inproceedings{Rodriguez:Barrow:Hoyle:Lalor:Jia:Boyd-Graber-2021,
	Title = {Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?},
	Author = {Pedro Rodriguez and Joe Barrow and Alexander Hoyle and John P. Lalor and Robin Jia and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2021},
	Url = {http://cs.umd.edu/~jbg//docs/2021_acl_leaderboard.pdf},
}
```
Accessible Abstract: When can we call an AI "intelligent"? Just like humans, a common approach is to ask them a bunch of questions. These questions posed to modern machine learning methods are collected in metrics called leaderboards to monitor progress, but beyond ranking approaches, this does not help us better understand our problems or our systems very well. This paper introduces probabilistic models inspired by psychometric approaches called item response theory models (think year-end standardized tests) to better understand how computers can answer questions and whether we are asking the right questions. This allows researchers to better compare what kinds of questions systems can answer, better compare human and machine ability, and discover problematic questions (e.g., questions that have incorrect answer keys, are vague, or "trick" those trying to answer the questions).
Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber, and Hal Daumé III. Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation. Empirical Methods in Natural Language Processing, 2021. [Code] [Research Talk] [Bibtex]
```
@inproceedings{Zhao:Xiong:Boyd-Graber:Daume-III-2021,
	Title = {Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation},
	Author = {Chen Zhao and Chenyan Xiong and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2021},
	Location = {Punta Cana},
	Url = {http://cs.umd.edu/~jbg//docs/2021_emnlp_weak_dpr.pdf},
}
```
Accessible Abstract: Answering questions sometimes requires tying multiple pieces of information together. Previous datasets have required annotators to explicitly build these reasoning chains (e.g., to answer "where do I know the cop from Die Hard from", you need to figure out that the actor's name is "Reginald VelJohnson" and then find out that he's best known as the dad on Family Matters.). By exploring search queries that get to the right answer, we're able to answer these questions without expensive annotation.
Pedro Rodriguez and Jordan Boyd-Graber. Evaluation Paradigms in Question Answering. Empirical Methods in Natural Language Processing, 2021. [Research Talk] [Bibtex]
```
@inproceedings{Rodriguez:Boyd-Graber-2021,
	Title = {Evaluation Paradigms in Question Answering},
	Author = {Pedro Rodriguez and Jordan Boyd-Graber},
	Location = {Punta Cana},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2021},
	Url = {http://cs.umd.edu/~jbg//docs/2021_emnlp_paradigms.pdf},
}
```
Accessible Abstract: Why do we answer questions? Sometimes it's to provide information, which has been the interpretation of the computer science community. But sometimes it's to probe or test intelligence. This paper argues we should think more about that application of question answering and its connection to the foundations of artificial intelligence: The Turing Test. We thus argue that in addition to the long-standing Cranfield paradigm popularized by information retrieval, this paper proposes an alternative "Manchester paradigm" closer to the Turing test, trivia games, and education.
Maharshi Gor, Kellie Webster, and Jordan Boyd-Graber. Toward Deconfounding the Influence of Subject's Demographic Characteristics in Question Answering. Empirical Methods in Natural Language Processing, 2021. [Research Talk] [Bibtex]
```
@inproceedings{Gor:Webster:Boyd-Graber-2021,
	Title = {Toward Deconfounding the Influence of Subject's Demographic Characteristics in Question Answering},
	Author = {Maharshi Gor and Kellie Webster and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2021},
	Location = {Punta Cana},
	Pages = {6},
	Url = {http://cs.umd.edu/~jbg//docs/2021_emnlp_qa_fairness.pdf},
}
```
Accessible Abstract: The data used to train computer question answering systems have three times as many men as women. This paper examines whether this is a problem for question answering accuracy. After a thorough investigation, we do not find evidence of serious accuracy discrepancies between languages. However, an absence of evidence is not evidence of absence, and we would argue that we need more diverse datasets to better represent the world's population.
Chenglei Si, Chen Zhao, and Jordan Boyd-Graber. What's in a Name? Answer Equivalence For Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2021. [Research Talk] [Bibtex]
```
@inproceedings{Si:Zhao:Boyd-Graber-2021,
	Title = {What's in a Name? Answer Equivalence For Open-Domain Question Answering},
	Author = {Chenglei Si and Chen Zhao and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2021},
	Location = {Punta Cana},
	Url = {http://cs.umd.edu/~jbg//docs/2021_emnlp_answer_equiv.pdf},
}
```
Accessible Abstract: Is Tim Cook the same person as Timothy Donald Cook? You might think so, but the way we train computers to answer questions would say they aren't. We show that keeping track of multiple names (and it's really simple) can create better question answering systems. Simply by adding alternate answers mined from knowledge bases, we can improve accuracy 1-2 points on major QA datasets.
Chenglei Si, Chen Zhao, and Jordan Boyd-Graber. What's in a Name? Answer Equivalence For Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2021. [Research Talk] [Bibtex]
```
@inproceedings{Si:Zhao:Boyd-Graber-2021,
	Title = {What's in a Name? Answer Equivalence For Open-Domain Question Answering},
	Author = {Chenglei Si and Chen Zhao and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2021},
	Location = {Punta Cana},
	Url = {http://cs.umd.edu/~jbg//docs/2021_emnlp_answer_equiv.pdf},
}
```
Accessible Abstract: Is Tim Cook the same person as Timothy Donald Cook? You might think so, but the way we train computers to answer questions would say they aren't. We show that keeping track of multiple names (and it's really simple) can create better question answering systems. Simply by adding alternate answers mined from knowledge bases, we can improve accuracy 1-2 points on major QA datasets.
Alexander Hoyle, Pranav Goel, Denis Peskov, Andrew Hian-Cheong, Jordan Boyd-Graber, and Philip Resnik. Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence. Neural Information Processing Systems, 2021. [ArXiv] [Research Talk (students)] [Research Talk (Jordan)] [Code] [Bibtex]
```
@inproceedings{Hoyle:Goel:Peskov:Hian-Cheong:Boyd-Graber:Resnik-2021,
	Title = {Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence},
	Author = {Alexander Hoyle and Pranav Goel and Denis Peskov and Andrew Hian-Cheong and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Neural Information Processing Systems},
	Location = {Online},
	Year = {2021},
	Url = {http://cs.umd.edu/~jbg//docs/2021_neurips_incoherence.pdf},
}
```
Accessible Abstract: Topic models help historians, journalists, and analysts make sense of large text collections. But how do you know if you have a good one? The field has settled on using "Automatic Coherence", but this paper argues that maybe that isn't the right choice if you want to actually make real users happy. This paper builds on our 2009 that showed perplexity was not a good evaluation of interpretability for topic models; while the field adopted automatic topic coherence as a result of that 2009 paper, this paper argues that automatic topic coherence is not a good metric for neural topic models (even though it worked for probabilistic topic models).
Julian Martin Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, and Jordan Boyd-Graber. Fool Me Twice: Entailment from Wikipedia Gamification. North American Association for Computational Linguistics, 2021. [Preprint] [Research Talk] [Code and Data] [Paper Read Aloud] [Play] [Bibtex]
```
@inproceedings{Eisenschlos:Dhingra:Bulian:B\"orschinger:Boyd-Graber-2021,
	Title = {Fool Me Twice: Entailment from Wikipedia Gamification},
	Author = {Julian Martin Eisenschlos and Bhuwan Dhingra and Jannis Bulian and Benjamin B\"orschinger and Jordan Boyd-Graber},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2021},
	Url = {http://cs.umd.edu/~jbg//docs/2021_naacl_fm2.pdf},
}
```
Accessible Abstract: Democracy and the free press depends on being able to recognize when facts online are true or not. For machine learning to help this critical problem, it needs good data identifying which statements are backed up by trusted sources and which are not. This research creates a game people can play online to craft difficult claims that can train computers to spot disinformation online.
Chen Zhao, Chenyan Xiong, Hal Daumé III, and Jordan Boyd-Graber. Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval. North American Association for Computational Linguistics, 2021. [Paper Read Aloud] [Bibtex]
```
@inproceedings{Zhao:Xiong:Daume-III:Boyd-Graber-2021,
	Title = {Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval},
	Author = {Chen Zhao and Chenyan Xiong and Hal {Daum\'{e} III} and Jordan Boyd-Graber},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2021},
	Url = {http://cs.umd.edu/~jbg//docs/2021_naacl_multi_ance.pdf},
}
```
Accessible Abstract: For computers to answer complicated questions online, they often need to put together multiple pieces of information (Ronald Reagan was both governor of California and an actor in Bedtime for Bonzo). However, existing approaches use the links in Wikipedia to combine these clues. This research helps computers find connected information without using these explicit links.

@inproceedings{Zhao:Xiong:Qian:Boyd-Graber-2020,
	Title = {Complex Factoid Question Answering with a Free-Text Knowledge Graph},
	Author = {Chen Zhao and Chenyan Xiong and Xin Qian and Jordan Boyd-Graber},
	Booktitle = {ACM International Conference on World Wide Web},
	Year = {2020},
	Location = {Taipei, Taiwan},
	Url = {http://cs.umd.edu/~jbg//docs/2020_www_delft.pdf},
}

Jordan Boyd-Graber and Benjamin Börschinger. What Question Answering can Learn from Trivia Nerds. Association for Computational Linguistics, 2020. [Preprint] [Audio of Paper Readthrough] [Video of Paper Readthrough] [ACL Presentation] [Bibtex]
```
@inproceedings{Boyd-Graber:B\"orschinger-2020,
	Title = {What Question Answering can Learn from Trivia Nerds},
	Author = {Jordan Boyd-Graber and Benjamin B\"orschinger},
	Year = {2020},
	Url = {http://cs.umd.edu/~jbg//docs/2020_acl_trivia.pdf},
	Location = {The Cyberverse Simulacrum of Seattle},
	Booktitle = {Association for Computational Linguistics},
}
```
Accessible Abstract: This paper reflects on the similarities between trivia competitions and computer question answering research. Modern machine learning requires large, quality datasets. The central thesis of this article argues that the same things that make trivia tournaments good (they're fun, fair, and consistently crown the best trivia players) can also improve question answering datasets. Concretely, we argue that question answering datasets should clearly specify what answers are requested, have systematic policies to deal with natural ambiguity and variation, have authors look at the data (and help others do the same), make sure questions separate the best from the rest, and ensure people can have fun. We draw on the authors' experience in the trivia community (including embarrassing episodes on Jeopardy!) to illustrate our arguments.

Tianze Shi, Chen Zhao, Jordan Boyd-Graber, Hal Daumé III, and Lillian Lee. On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries. Findings of EMNLP, 2020. [Data] [Preprint] [Bibtex]

@article{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
	Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries},
	Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
	Journal = {Findings of EMNLP},
	Year = {2020},
	Url = {http://cs.umd.edu/~jbg//docs/2020_findings_qalign.pdf},
}

Diggelmann, Thomas, Boyd-Graber, Jordan, Bulian, Jannis, Ciaramita, Massimiliano, and Leippold, Markus. CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims. NIPS Workshop on Tackling Climate Change with Machine Learning, 2020. [Dataset] [Bibtex]

@article{Thomas:Jordan:Jannis:Massimiliano:Markus-2020,
	Title = {CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims},
	Author = {Diggelmann, Thomas and Boyd-Graber, Jordan and Bulian, Jannis and Ciaramita, Massimiliano and Leippold, Markus},
	Booktitle = {NIPS Workshop on Tackling Climate Change with Machine Learning},
	Year = {2020},
	Url = {https://research.google/pubs/pub50541/},
}

Eric Wallace, Shi Feng, and Jordan Boyd-Graber. Misleading Failures of Partial-input Baselines. Association for Computational Linguistics, 2019. [Bibtex]

@inproceedings{Wallace:Feng:Boyd-Graber-2019,
	Title = {Misleading Failures of Partial-input Baselines},
	Author = {Eric Wallace and Shi Feng and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2019},
	Location = {Florence, Italy},
	Url = {http://cs.umd.edu/~jbg//docs/2019_acl_flipside.pdf},
}

Ahmed Elgohary Ghoneim, Denis Peskov, and Jordan Boyd-Graber. Can You Unpack That? Learning to Rewrite Questions-in-Context. Empirical Methods in Natural Language Processing, 2019. [Data] [Bibtex]

@inproceedings{Elgohary:Peskov:Boyd-Graber-2019,
	Title = {Can You Unpack That? Learning to Rewrite Questions-in-Context},
	Author = {Ahmed Elgohary and Denis Peskov and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2019},
	Location = {Hong Kong, China},
	Url = {http://cs.umd.edu/~jbg//docs/2019_emnlp_sequentialqa.pdf},
}

Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. [Bibtex]

@inproceedings{Feng:Boyd-Graber-2019,
	Title = {What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play},
	Author = {Shi Feng and Jordan Boyd-Graber},
	Booktitle = {Intelligent User Interfaces},
	Year = {2019},
	Location = {Los Angeles, CA},
	Url = {http://cs.umd.edu/~jbg//docs/2019_iui_augment.pdf},
}

Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Transactions of the Association for Computational Linguistics, 2019. [Code] [Videos] [Data] [Bibtex]

@article{Wallace:Rodriguez:Feng:Yamada:Boyd-Graber-2019,
	Title = {Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples},
	Author = {Eric Wallace and Pedro Rodriguez and Shi Feng and Ikuya Yamada and Jordan Boyd-Graber},
	Booktitle = {Transactions of the Association for Computational Linguistics},
	Year = {2019},
	Volume = {10},
	Url = {http://cs.umd.edu/~jbg//docs/2019_tacl_trick.pdf},
}

Eric Wallace and Jordan Boyd-Graber. Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. ACL Student Research Workshop, 2018. [Bibtex]

@inproceedings{Wallace:Boyd-Graber-2018,
	Title = {Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions},
	Author = {Eric Wallace and Jordan Boyd-Graber},
	Booktitle = {ACL Student Research Workshop},
	Year = {2018},
	Location = {Melbourne, Australia},
	Url = {http://aclweb.org/anthology/P18-3018},
}

Shi Feng, Eric Wallace, and Jordan Boyd-Graber. Interpreting Neural Networks with Nearest Neighbors. EMNLP Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2018. [Bibtex]

@inproceedings{Feng:Wallace:Boyd-Graber-2018,
	Title = {Interpreting Neural Networks with Nearest Neighbors},
	Author = {Shi Feng and Eric Wallace and Jordan Boyd-Graber},
	Booktitle = {EMNLP Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP},
	Year = {2018},
	Location = {Brussels, Belgium},
	Url = {http://aclweb.org/anthology/W18-5416},
}

Ahmed Elgohary Ghoneim, Chen Zhao, and Jordan Boyd-Graber. Dataset and Baselines for Sequential Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2018. [Data] [Bibtex]

@inproceedings{Elgohary:Zhao:Boyd-Graber-2018,
	Title = {Dataset and Baselines for Sequential Open-Domain Question Answering},
	Author = {Ahmed Elgohary and Chen Zhao and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2018},
	Location = {Brussels, Belgium},
	Url = {http://cs.umd.edu/~jbg//docs/2018_emnlp_linked.pdf},
}

Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. [Blog Post] [Bibtex]

@inproceedings{Feng:Wallace:II:Rodriguez:Iyyer:Boyd-Graber-2018,
	Title = {Pathologies of Neural Models Make Interpretation Difficult},
	Author = {Shi Feng and Eric Wallace and Alvin Grissom II and Pedro Rodriguez and Mohit Iyyer and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2018},
	Location = {Brussels, Belgium},
	Url = {http://cs.umd.edu/~jbg//docs/2018_emnlp_rs.pdf},
}

Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association for Computational Linguistics, 2018. [Bibtex]

@inproceedings{Iyyer:Manjunatha:Boyd-Graber:Davis-2018,
	Title = {Learning to Color from Language},
	Author = {Mohit Iyyer and Varun Manjunatha and Jordan Boyd-Graber and Larry Davis},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2018},
	Url = {http://cs.umd.edu/~jbg//docs/2018_naacl_colorization.pdf},
}

Jordan Boyd-Graber, Shi Feng, and Pedro Rodriguez. Human-Computer Question Answering: The Case for Quizbowl. The NIPS '17 Competition: Building Intelligent Systems, 2018. [Bibtex]

@inbook{Boyd-Graber:Feng:Rodriguez-2018,
	Editor = {Sergio Escalera and Markus Weimer},
	Title = {Human-Computer Question Answering: The Case for Quizbowl},
	Author = {Jordan Boyd-Graber and Shi Feng and Pedro Rodriguez},
	Booktitle = {The NIPS '17 Competition: Building Intelligent Systems},
	Publisher = {Springer Verlag},
	Year = {2018},
	Url = {http://cs.umd.edu/~jbg//docs/2018_nips_qbcomp.pdf},
}

Closing the Loop

Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. [Webpage] [Bibtex]

@article{Rodriguez:Feng:Iyyer:He:Boyd-Graber-Preprint,
	Title = {Quizbowl: The Case for Incremental Question Answering},
	Author = {Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan Boyd-Graber},
	Journal = {ArXiv},
	Year = {Preprint},
	Url = {https://arxiv.org/abs/1904.04792},
}

Alison Smith, Jordan Boyd-Graber, Ron Fan, Melissa Birchfield, Tongshuang Wu, Dan Weld, and Leah Findlater. No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML. Computer-Human Interaction, 2020. [Bibtex]

@inproceedings{Smith:Boyd-Graber:Fan:Birchfield:Wu:Weld:Findlater-2020,
	Author = {Alison Smith and Jordan Boyd-Graber and Ron Fan and Melissa Birchfield and Tongshuang Wu and Dan Weld and Leah Findlater},
	Booktitle = {Computer-Human Interaction},
	Year = {2020},
	Url = {http://cs.umd.edu/~jbg//docs/2020_chi_explanation.pdf},
	Title = {No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML},
}

Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Digging into User Control: Perceptions of Adherence and Instability in Transparent Models. Intelligent User Interfaces, 2020. [Bibtex]

@inproceedings{Smith:Kumar:Boyd-Graber:Seppi:Findlater-2020,
	Author = {Alison Smith and Varun Kumar and Jordan Boyd-Graber and Kevin Seppi and Leah Findlater},
	Booktitle = {Intelligent User Interfaces},
	Url = {http://cs.umd.edu/~jbg//docs/2020_iui_control.pdf},
	Year = {2020},
	Title = {Digging into User Control: Perceptions of Adherence and
Instability in Transparent Models},
}

Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater. Which Evaluations Uncover Sense Representations that Actually Make Sense?. Linguistic Resources and Evaluation Conference, 2020. [Bibtex]

@inproceedings{Guo:Boyd-Graber:Iyyer:Findlater-2020,
	Author = {Fenfei Guo and Jordan Boyd-Graber and Mohit Iyyer and Leah Findlater},
	Location = {France (but only in dreams)},
	Booktitle = {Linguistic Resources and Evaluation Conference},
	Url = {http://cs.umd.edu/~jbg//docs/2020_lrec_sense.pdf},
	Year = {2020},
	Title = {Which Evaluations Uncover Sense Representations that Actually Make Sense?},
}

Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, and Kevin Seppi. Automatic and Human Evaluation of Local Topic Quality. Association for Computational Linguistics, 2019. [Code] [Bibtex]

@inproceedings{Lund:Armstrong:Fearn:Cowley:Byun:Boyd-Graber:Seppi-2019,
	Title = {Automatic and Human Evaluation of Local Topic Quality},
	Author = {Jeffrey Lund and Piper Armstrong and Wilson Fearn and Stephen Cowley and Courtni Byun and Jordan Boyd-Graber and Kevin Seppi},
	Booktitle = {Association for Computational Linguistics},
	Year = {2019},
	Location = {Florence, Italy},
	Url = {http://cs.umd.edu/~jbg//docs/2019_acl_local.pdf},
}

Varun Kumar, Alison Smith, Leah Findlater, Kevin Seppi, and Jordan Boyd-Graber. Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models. Association for Computational Linguistics, 2019. [Bibtex]

@inproceedings{Kumar:Smith:Findlater:Seppi:Boyd-Graber-2019,
	Author = {Varun Kumar and Alison Smith and Leah Findlater and Kevin Seppi and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2019},
	Location = {Florence, Italy},
	Title = {Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models},
	Url = {http://cs.umd.edu/~jbg//docs/2019_acl_control.pdf},
}

Dasha Pruss, Yoshinari Fujinuma, Ashlynn Daughton, Michael Paul, Brad Arnot, Danielle Szafir, and Jordan Boyd-Graber. Zika discourse in the Americas: A multilingual topic analysis of Twitter. PlosOne, 2019. [Data] [Bibtex]

@article{Pruss:Fujinuma:Daughton:Paul:Arnot:Szafir:Boyd-Graber-2019,
	Author = {Dasha Pruss and Yoshinari Fujinuma and Ashlynn Daughton and Michael Paul and Brad Arnot and Danielle Szafir and Jordan Boyd-Graber},
	Journal = {PlosOne},
	Year = {2019},
	Title = {Zika discourse in the Americas: A multilingual topic analysis of {Twitter}},
	Url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216922},
}

@article{Wallace:Rodriguez:Feng:Yamada:Boyd-Graber-2019,
	Title = {Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples},
	Author = {Eric Wallace and Pedro Rodriguez and Shi Feng and Ikuya Yamada and Jordan Boyd-Graber},
	Booktitle = {Transactions of the Association for Computational Linguistics},
	Year = {2019},
	Volume = {10},
	Url = {http://cs.umd.edu/~jbg//docs/2019_tacl_trick.pdf},
}

Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018. [Bibtex]

@inproceedings{Smith:Kumar:Boyd-Graber:Seppi:Findlater-2018,
	Author = {Alison Smith and Varun Kumar and Jordan Boyd-Graber and Kevin Seppi and Leah Findlater},
	Booktitle = {Intelligent User Interfaces},
	Year = {2018},
	Title = {User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System},
	Url = {http://cs.umd.edu/~jbg//docs/2018_iui_itm.pdf},
}

Alison won a best student paper honorable mention (3 out of 300)

Paul Felt, Eric Ringger, Kevin Seppi, and Jordan Boyd-Graber. Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types. International Conference on Computational Linguistics, 2018. [Bibtex]

@inproceedings{Felt:Ringger:Seppi:Boyd-Graber-2018,
	Title = {Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types},
	Author = {Paul Felt and Eric Ringger and Kevin Seppi and Jordan Boyd-Graber},
	Booktitle = {International Conference on Computational Linguistics},
	Year = {2018},
	Location = {Santa Fe, New Mexico},
	Url = {http://cs.umd.edu/~jbg//docs/2018_coling_measurements.pdf},
}

Jeff Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber. Tandem Anchoring: A Multiword Anchor Approach for Interactive Topic Modeling. Association for Computational Linguistics, 2017. [Code] [Bibtex]

@inproceedings{Lund:Cook:Seppi:Boyd-Graber-2017,
	Title = {Tandem Anchoring: A Multiword Anchor Approach for Interactive Topic Modeling},
	Author = {Jeff Lund and Connor Cook and Kevin Seppi and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2017},
	Location = {Vancouver, British Columbia},
	Url = {http://cs.umd.edu/~jbg//docs/2017_acl_multiword_anchors.pdf},
}

Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. [Bibtex]

@inproceedings{Smith:Kumar:Boyd-Graber:Seppi:Findlater-2017,
	Author = {Alison Smith and Varun Kumar and Jordan Boyd-Graber and Kevin Seppi and Leah Findlater},
	Booktitle = {CHI 2017 Designing for Uncertainty Workshop},
	Year = {2017},
	Location = {Denver, CO},
	Title = {Accounting for Input Uncertainty in Human-in-the-Loop Systems},
	Url = {http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf},
}

You Lu, Jeff Lund, and Jordan Boyd-Graber. Why ADAGRAD Fails for Online Topic Modeling. Empirical Methods in Natural Language Processing, 2017. [Bibtex]

@inproceedings{Lu:Lund:Boyd-Graber-2017,
	Title = {Why ADAGRAD Fails for Online Topic Modeling},
	Author = {You Lu and Jeff Lund and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Url = {http://cs.umd.edu/~jbg//docs/2017_emnlp_adagrad_olda.pdf},
	Year = {2017},
	Location = {Copenhagen, Denmark},
}

Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. [Journal] [Bibtex]

@article{Lee:Smith:Seppi:Elmqvist:Boyd-Graber:Findlater-2017,
	Author = {Tak Yeon Lee and Alison Smith and Kevin Seppi and Niklas Elmqvist and Jordan Boyd-Graber and Leah Findlater},
	Journal = {International Journal of Human-Computer Studies},
	Year = {2017},
	Title = {The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models},
	Url = {http://cs.umd.edu/~jbg//docs/2017_ijhcs_human_touch.pdf},
}

Jordan Boyd-Graber. Humans and Computers Working Together to Measure Machine Learning Interpretability. The Bridge, 2017. [Journal] [Bibtex]

@article{Boyd-Graber-2017,
	Author = {Jordan Boyd-Graber},
	Journal = {The Bridge},
	Year = {2017},
	Title = {Humans and Computers Working Together to Measure Machine Learning Interpretability},
	Volume = {47},
	Pages = {6--10},
}

Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. [Journal] [Data] [Bibtex]

@article{Smith:Lee:Poursabzi-Sangdeh:Boyd-Graber:Seppi:Elmqvist:Findlater-2017,
	Author = {Alison Smith and Tak Yeon Lee and Forough Poursabzi-Sangdeh and Jordan Boyd-Graber and Kevin Seppi and Niklas Elmqvist and Leah Findlater},
	Journal = {Transactions of the Association for Computational Linguistics},
	Year = {2017},
	Title = {Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels},
	Volume = {5},
	Url = {http://cs.umd.edu/~jbg//docs/2017_tacl_eval_tm_viz.pdf},
	Pages = {1--15},
}

Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, and Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. Association for Computational Linguistics, 2016. [Code] [Bibtex]

@inproceedings{Poursabzi-Sangdeh:Boyd-Graber:Findlater:Seppi-2016,
	Title = {ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling},
	Author = {Forough Poursabzi-Sangdeh and Jordan Boyd-Graber and Leah Findlater and Kevin Seppi},
	Booktitle = {Association for Computational Linguistics},
	Year = {2016},
	Location = {Berlin, Brandenburg},
	Url = {http://cs.umd.edu/~jbg//docs/2016_acl_doclabel.pdf},
}

Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. [Bibtex]

@inproceedings{Smith:Lee:Poursabzi-Sangdeh:Boyd-Graber:Seppi:Elmqvist:Findlater-2016,
	Author = {Alison Smith and Tak Yeon Lee and Forough Poursabzi-Sangdeh and Jordan Boyd-Graber and Kevin Seppi and Niklas Elmqvist and Leah Findlater},
	Booktitle = {CHI Human Centred Machine Learning Workshop},
	Year = {2016},
	Location = {San Jose, CA},
	Title = {Human-Centered and Interactive: Expanding the Impact of Topic Models},
}

Md Arafat Sultan, Jordan Boyd-Graber, and Tamara Sumner. Bayesian Supervised Domain Adaptation for Short Text Similarity. North American Association for Computational Linguistics, 2016. [Talk] [Bibtex]

@inproceedings{Sultan:Boyd-Graber:Sumner-2016,
	Title = {Bayesian Supervised Domain Adaptation for Short Text Similarity},
	Author = {Md Arafat Sultan and Jordan Boyd-Graber and Tamara Sumner},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2016},
	Location = {San Diego, CA},
	Url = {http://cs.umd.edu/~jbg//docs/2016_naacl_sts.pdf},
}

Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler. Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. Association for Computational Linguistics, 2015. [Talk] [Code] [LaTeX] [Bibtex]
```
@inproceedings{Nguyen:Boyd-Graber:Resnik:Miler-2015,
	Title = {Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress},
	Author = {Viet-An Nguyen and Jordan Boyd-Graber and Philip Resnik and Kristina Miler},
	Booktitle = {Association for Computational Linguistics},
	Year = {2015},
	Location = {Beijing, China},
	Url = {http://cs.umd.edu/~jbg//docs/2015_acl_teaparty.pdf},
}
```
Accessible Abstract: In the mid 2010s, the Republican party in the United States diverged: mainstream conservatives split from the so-called "tea party" caucus. However, the primary statistical tool for analyzing political factions in legislative bodies (ideal point models) fail to account for these changes. This is because the schism is not fully reflected in voting patterns but rather in how politicians present themselves: thus we need to extend these models to capture not just how politicians vote but also how they frame particular issues. This paper proposes a new model to capture framing differences within a voting block to start explaining the new subcoalitions of the republican caucus.

Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. [Talk] [Bibtex]

@inproceedings{Felt:Ringger:Boyd-Graber:Seppi-2015,
	Title = {Making the Most of Crowdsourced Document Annotations: Confused Supervised {LDA}},
	Author = {Paul Felt and Eric Ringger and Jordan Boyd-Graber and Kevin Seppi},
	Booktitle = {Conference on Computational Natural Language Learning},
	Year = {2015},
	Location = {Beijing, China},
	Url = {http://cs.umd.edu/~jbg//docs/2015_conll_cslda.pdf},
}

This paper received the best paper award at CoNLL

Yi Yang, Doug Downey, and Jordan Boyd-Graber. Efficient Methods for Incorporating Knowledge into Topic Models. Empirical Methods in Natural Language Processing, 2015. [Code] [Bibtex]

@inproceedings{Yang:Downey:Boyd-Graber-2015,
	Title = {Efficient Methods for Incorporating Knowledge into Topic Models},
	Author = {Yi Yang and Doug Downey and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2015},
	Location = {Lisbon, Portugal},
	Url = {http://cs.umd.edu/~jbg//docs/2015_emnlp_fast_priors.pdf},
}

Stephen H. Bach, Bert Huang, Jordan Boyd-Graber, and Lise Getoor. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning, 2015. [Video] [Bibtex]

@inproceedings{Bach:Huang:Boyd-Graber:Getoor-2015,
	Title = {Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs},
	Author = {Stephen H. Bach and Bert Huang and Jordan Boyd-Graber and Lise Getoor},
	Location = {Lille, France},
	Booktitle = {International Conference on Machine Learning},
	Year = {2015},
	Url = {http://cs.umd.edu/~jbg//docs/2015_icml_paired_dual.pdf},
}

Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. [Bibtex]

@inproceedings{Nguyen:Boyd-Graber:Lund:Seppi:Ringger-2015,
	Title = {Is your anchor going up or down?  {F}ast and accurate supervised topic models},
	Author = {Thang Nguyen and Jordan Boyd-Graber and Jeff Lund and Kevin Seppi and Eric Ringger},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2015},
	Location = {Denver, Colorado},
	Url = {http://cs.umd.edu/~jbg//docs/2015_naacl_supervised_anchor.pdf},
}

Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. [Bibtex]

@inproceedings{Smith:Chuang:Hu:Boyd-Graber:Findlater-2014,
	Title = {Concurrent Visualization of Relationships between Words and Topics in Topic Models},
	Author = {Alison Smith and Jason Chuang and Yuening Hu and Jordan Boyd-Graber and Leah Findlater},
	Booktitle = {ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces},
	Year = {2014},
	Location = {Baltimore, Maryland},
}

Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. [Journal] [Frontend Code] [Backend Code] [Bibtex]

@article{Hu:Boyd-Graber:Satinoff:Smith-2014,
	Title = {Interactive Topic Modeling},
	Author = {Yuening Hu and Jordan Boyd-Graber and Brianna Satinoff and Alison Smith},
	Journal = {Machine Learning},
	Url = {http://cs.umd.edu/~jbg//docs/2014_mlj_itm.pdf},
	Year = {2014},
	Volume = {95},
	Pages = {423--469},
	Publisher = {Springer},
}

Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. [Slides] [Code] [Bibtex]

@inproceedings{Hu:Boyd-Graber:Satinoff-2011,
	Title = {Interactive Topic Modeling},
	Author = {Yuening Hu and Jordan Boyd-Graber and Brianna Satinoff},
	Booktitle = {Association for Computational Linguistics},
	Year = {2011},
	Location = {Portland, Oregon},
	Url = {http://cs.umd.edu/~jbg//docs/itm.pdf},
}

Cross-Language Bayesian Models for Web-Scale Text Analysis

Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms. Association for Computational Linguistics, 2014. [Talk] [Bibtex]

@inproceedings{Nguyen:Hu:Boyd-Graber-2014,
	Title = {Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms},
	Author = {Thang Nguyen and Yuening Hu and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2014},
	Location = {Baltimore, MD},
	Url = {http://cs.umd.edu/~jbg//docs/2014_acl_anchor_reg.pdf},
}

Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. [Data] [Bibtex]

@inproceedings{Iyyer:Enns:Boyd-Graber:Resnik-2014,
	Title = {Political Ideology Detection Using Recursive Neural Networks},
	Author = {Mohit Iyyer and Peter Enns and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Association for Computational Linguistics},
	Year = {2014},
	Location = {Baltimore, MD},
	Url = {http://cs.umd.edu/~jbg//docs/2014_acl_rnn_ideology.pdf},
}

Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. [Journal] [Frontend Code] [Backend Code] [Bibtex]

@article{Hu:Boyd-Graber:Satinoff:Smith-2014,
	Title = {Interactive Topic Modeling},
	Author = {Yuening Hu and Jordan Boyd-Graber and Brianna Satinoff and Alison Smith},
	Journal = {Machine Learning},
	Url = {http://cs.umd.edu/~jbg//docs/2014_mlj_itm.pdf},
	Year = {2014},
	Volume = {95},
	Pages = {423--469},
	Publisher = {Springer},
}

Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah Cai, Jennifer Midberry, and Yuanxin Wang. Modeling Topic Control to Detect Influence in Conversations using Nonparametric Topic Models. Machine Learning, 2014. [Journal] [Code] [Data] [Bibtex]

@article{Nguyen:Boyd-Graber:Resnik:Cai:Midberry:Wang-2014,
	Title = {Modeling Topic Control to Detect Influence in Conversations using Nonparametric Topic Models},
	Author = {Viet-An Nguyen and Jordan Boyd-Graber and Philip Resnik and Deborah Cai and Jennifer Midberry and Yuanxin Wang},
	Url = {http://cs.umd.edu/~jbg//docs/2014_mlj_influencer.pdf},
	Journal = {Machine Learning},
	Year = {2014},
	Volume = {95},
	Pages = {381--421},
	Publisher = {Springer},
}

Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. [Bibtex]

@article{Zhai:Boyd-Graber:Cohen-2014,
	Title = {Hybrid Online Inference with Adaptor Grammars},
	Author = {Ke Zhai and Jordan Boyd-Graber and Shay B. Cohen},
	Booktitle = {NIPS Workshop on Advances in Variational Inference},
	Year = {2014},
}

Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. [Code] [Bibtex]

@article{Zhai:Boyd-Graber:Cohen-2014,
	Title = {Online Adaptor Grammars with Hybrid Inference},
	Author = {Ke Zhai and Jordan Boyd-Graber and Shay B. Cohen},
	Journal = {Transactions of the Association for Computational Linguistics},
	Year = {2014},
	Publisher = {Association for Computational Linguistics},
	Url = {http://cs.umd.edu/~jbg//docs/2014_tacl_ag_vb_online.pdf},
}

Jordan Boyd-Graber, Kimberly Glasgow, and Jackie Sauter Zajac. Spoiler Alert: Machine Learning Approaches to Detect Social Media Posts with Revelatory Information. ASIST 2013: The 76th Annual Meeting of the American Society for Information Science and Technology, 2013. [Data] [Bibtex]

@inproceedings{Boyd-Graber:Glasgow:Zajac-2013,
	Title = {Spoiler Alert: Machine Learning Approaches to Detect Social Media Posts with Revelatory Information},
	Author = {Jordan Boyd-Graber and Kimberly Glasgow and Jackie Sauter Zajac},
	Booktitle = {ASIST 2013: The 76th Annual Meeting of the American Society for Information Science and Technology},
	Year = {2013},
	Location = {Montreal, Canada},
	Url = {http://cs.umd.edu/~jbg//docs/2013_spoiler.pdf},
}

Ke Zhai and Jordan Boyd-Graber. Online Topic Models with Infinite Vocabulary. International Conference on Machine Learning, 2013. [Poster] [Talk] [Code] [Bibtex]

@inproceedings{Zhai:Boyd-Graber-2013,
	Title = {Online Topic Models with Infinite Vocabulary},
	Author = {Ke Zhai and Jordan Boyd-Graber},
	Booktitle = {International Conference on Machine Learning},
	Year = {2013},
	Url = {http://cs.umd.edu/~jbg//docs/2013_icml_infvoc.pdf},
}

Viet-An Nguyen, Jordan Boyd-Graber, and Stephen Altschul. Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein Space. Journal of Computational Biology, 2013. [Journal] [Bibtex]

@article{Nguyen:Boyd-Graber:Altschul-2013,
	Title = {Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein Space},
	Author = {Viet-An Nguyen and Jordan Boyd-Graber and Stephen Altschul},
	Journal = {Journal of Computational Biology},
	Year = {2013},
	Volume = {20},
	Number = {1},
	Url = {http://cs.umd.edu/~jbg//docs/2013_dp_protein.pdf},
}

Yuening Hu, Jordan Boyd-Graber, Hal Daumé III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. [Supplement] [Data] [Bibtex]

@inproceedings{Hu:Boyd-Graber:Daume-III:Ying-2013,
	Url = {http://cs.umd.edu/~jbg//docs/2013_coalescent.pdf},
	Title = {Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent},
	Author = {Yuening Hu and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Z. Irene Ying},
	Booktitle = {Neural Information Processing Systems},
	Year = {2013},
}

Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. [Supplement] [Bibtex]

@inproceedings{Nguyen:Boyd-Graber:Resnik-2013,
	Url = {http://cs.umd.edu/~jbg//docs/2013_shlda.pdf},
	Title = {Lexical and Hierarchical Topic Regression},
	Author = {Viet-An Nguyen and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Neural Information Processing Systems},
	Year = {2013},
	Location = {Lake Tahoe, Nevada},
}

Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. [Bibtex]

@inproceedings{Nguyen:Hu:Boyd-Graber:Resnik-2013,
	Title = {Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations},
	Author = {Viet-An Nguyen and Yuening Hu and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2013},
	Location = {Atlanta Georgia},
	Url = {http://cs.umd.edu/~jbg//docs/2013_argviz.pdf},
}

Naho Orita, Rebecca McKeown, Naomi H. Feldman, Jeffrey Lidz, and Jordan Boyd-Graber. Discovering Pronoun Categories using Discourse Information. Proceedings of the Cognitive Science Society, 2013. [Bibtex]

@inproceedings{Orita:McKeown:Feldman:Lidz:Boyd-Graber-2013,
	Title = {Discovering Pronoun Categories using Discourse Information},
	Author = {Naho Orita and Rebecca McKeown and Naomi H. Feldman and Jeffrey Lidz and Jordan Boyd-Graber},
	Booktitle = {Proceedings of the Cognitive Science Society},
	Year = {2013},
	Location = {Berlin, Germany},
	Url = {http://cs.umd.edu/~jbg//docs/2013_cogsci_pronoun.pdf},
}

Ke Zhai, Jordan Boyd-Graber, Nima Asadi, and Mohamad (Jude) Alkhouja. Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce. ACM International Conference on World Wide Web, 2012. [Code] [Slides] [Bibtex]

@inproceedings{Zhai:Boyd-Graber:Asadi:Alkhouja-2012,
	Title = {{Mr. LDA}: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce},
	Url = {http://cs.umd.edu/~jbg//docs/2012_www_mrlda.pdf},
	Author = {Ke Zhai and Jordan Boyd-Graber and Nima Asadi and Mohamad Alkhouja},
	Booktitle = {ACM International Conference on World Wide Web},
	Year = {2012},
	Location = {Lyon, France},
}

Yuening Hu and Jordan Boyd-Graber. Efficient Tree-Based Topic Modeling. Association for Computational Linguistics, 2012. [Bibtex]

@inproceedings{Hu:Boyd-Graber-2012,
	Title = {Efficient Tree-Based Topic Modeling},
	Author = {Yuening Hu and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2012},
	Location = {Jeju, South Korea},
	Url = {http://cs.umd.edu/~jbg//docs/acl_2012_fttm.pdf},
}

Vladimir Eidelman, Jordan Boyd-Graber, and Philip Resnik. Topic Models for Dynamic Translation Model Adaptation. Association for Computational Linguistics, 2012. [Presentation] [More Recent Paper] [Bibtex]
```
@inproceedings{Eidelman:Boyd-Graber:Resnik-2012,
	Title = {Topic Models for Dynamic Translation Model Adaptation},
	Author = {Vladimir Eidelman and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Association for Computational Linguistics},
	Year = {2012},
	Location = {Jeju, South Korea},
	Url = {http://cs.umd.edu/~jbg//docs/acl_2012_tm_for_mt.pdf},
}
```
For a more thorough evaluation and an exploration of more advanced topic models for machine translation, see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014.

Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations. Association for Computational Linguistics, 2012. [Data] [Code] [Slides] [Appendix] [Bibtex]

@inproceedings{Nguyen:Boyd-Graber:Resnik-2012,
	Title = {{SITS}: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations},
	Author = {Viet-An Nguyen and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Association for Computational Linguistics},
	Year = {2012},
	Location = {Jeju, South Korea},
	Url = {http://cs.umd.edu/~jbg//docs/acl_2012_sits.pdf},
}

Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference on Machine Learning, 2012. [Code] [Data] [Research Talk] [Bibtex]

@inproceedings{Hu:Zhai:Williamson:Boyd-Graber-2012,
	Title = {Modeling Images using Transformed {I}ndian Buffet Processes},
	Author = {Yuening Hu and Ke Zhai and Sinead Williamson and Jordan Boyd-Graber},
	Booktitle = {International Conference on Machine Learning},
	Year = {2012},
	Url = {http://cs.umd.edu/~jbg//docs/mtibp_icml_2012.pdf},
	Location = {Edinburgh, Scotland},
}

DADC

HyoJung Han, Marine Carpuat, and Jordan Boyd-Graber. Automatic Explicitation to Bridge the Background Knowledge Gap in Translation and its Evaluation with Multilingual QA. Empirical Methods in Natural Language Processing, 2023. [Video] [Bibtex]
```
@inproceedings{Han:Carpuat:Boyd-Graber-2023,
	Title = {Automatic Explicitation to Bridge the Background Knowledge Gap in Translation and its Evaluation with Multilingual QA},
	Author = {HyoJung Han and Marine Carpuat and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2023},
	Location = {Singapore},
	Url = {http://cs.umd.edu/~jbg//docs/2023_emnlp_explicitation.pdf},
}
```
Accessible Abstract: Sometimes when you a translating from one language to another, a literal translation is not enough. Sometimes to actually understand what is being said, you need additional context. Professional translators know this, and the process that they use to help a listener is called "explicitation" to capturing cultural differences between source and target audiences. We introduce techniques for automatically generating explicitations, motivated by WikiExpl(a dataset collected from Wikipedia and annotate with human translators), and evaluate the explicitation.
Sander V Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Jordan Lee Boyd-Graber, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, and Christopher R Carnahan. Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition. Empirical Methods in Natural Language Processing, 2023. [Prerecorded Video] [Data] [Award Video] [Bibtex]
```
@inproceedings{Schulhoff:Pinto:Khan:Bouchard:Si:Boyd-Graber:Anati:Tagliabue:Kost:Carnahan-2023,
	Title = {Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition},
	Author = {Sander V Schulhoff and Jeremy Pinto and Anaum Khan and Louis-François Bouchard and Chenglei Si and Jordan Lee Boyd-Graber and Svetlina Anati and Valen Tagliabue and Anson Liu Kost and Christopher R Carnahan},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2023},
	Location = {Singapore},
	Url = {http://cs.umd.edu/~jbg//docs/2023_emnlp_hackaprompt.pdf},
}
```
This paper was selected as the Best Theme Paper at EMNLP 2023 (1 of 4909)

Accessible Abstract: As more AI services online are provided by prompted language models, we need to be aware of the weaknesses and exploits of the models. We present the HackAPrompt competition to help elicit a broad array of exploits that get around large langauge models.
Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, and Jordan Boyd-Graber. Getting MoRE out of Mixture of Language Model Reasoning Experts. Findings of Empirical Methods in Natural Language Processing, 2023. [Video] [Bibtex]
```
@article{Si:Shi:Zhao:Zettlemoyer:Boyd-Graber-2023,
	Title = {Getting \underline{MoRE} out of \underline{M}ixture \underline{o}f Language Model \underline{R}easoning \underline{E}xperts},
	Author = {Chenglei Si and Weijia Shi and Chen Zhao and Luke Zettlemoyer and Jordan Boyd-Graber},
	Journal = {Findings of Empirical Methods in Natural Language Processing},
	Year = {2023},
	Location = {Singapore},
	Url = {http://cs.umd.edu/~jbg//docs/2023_findings_more.pdf},
}
```
Accessible Abstract: There are many ways for a computer to answer a question: a general knowledge question, a common sense question, or a math question. Each of these types of questions can be answered by a particular kind of expert. This paper investigates if we can automatically detect what kind of expert is best suited to answer a question and route the question to the correct expert.

Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. Prompting GPT-3 To Be Reliable. International Conference on Learning Representations, 2023. [Code] [Bibtex]

@inproceedings{Si:Gan:Yang:Wang:Wang:Boyd-Graber:Wang-2023,
	Title = {Prompting GPT-3 To Be Reliable},
	Author = {Chenglei Si and Zhe Gan and Zhengyuan Yang and Shuohang Wang and Jianfeng Wang and Jordan Boyd-Graber and Lijuan Wang},
	Booktitle = {International Conference on Learning Representations},
	Year = {2023},
	Url = {http://cs.umd.edu/~jbg//docs/2023_iclr_reliable.pdf},
}

DADC

Yoo Yeon Sung, Naeemul Hassan, and Jordan Boyd-Graber. Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines. Empirical Methods in Natural Language Processing, 2023. [Video] [Bibtex]
```
@inproceedings{Sung:Hassan:Boyd-Graber-2023,
	Title = {Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines},
	Author = {Yoo Yeon Sung and Naeemul Hassan and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2023},
	Location = {Singapore},
	Url = {http://cs.umd.edu/~jbg//docs/2023_emnlp_videoheadline.pdf},
}
```
Accessible Abstract: Misinformation online is not all text-based. More information is being consumed in video form, and both social media companies and external monitors need to know when misleading videos are being shared online. We create a new dataset of misleading videos and describe what makes the problem so challenging.

FACT

Wichayaporn Wongkamjan and Feng Gu and Yanze Wang and Ulf Hermjakob and Jonathan May and Brandon M. Stewart and Jonathan K. Kummerfeld and Denis Peskoff and Jordan Lee Boyd-Graber. More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play. Association for Computational Linguistics, 2024. [Bibtex]
```
@inproceedings{Boyd-Graber-2024,
	Title = {More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play},
	Booktitle = {Association for Computational Linguistics},
	Year = {2024},
	Location = {Bangkok, Thailand},
	Url = {http://cs.umd.edu/~jbg//docs/2024_acl_cicero.pdf},
}
```
Accessible Abstract: Meta's recent AI, Cicero, grabbed headlines by its ability to beat humans at the game of Diplomacy: notable because players of the game not just need to make the right moves but also need to negotiate with each other in natural language. This paper investigates why it wins so many games, measuring its ability to persuade and trick other players. While Cicero wins just about every game, this is because of superhuman strategy, not superhuman communication, suggesting there is still further room for developing Diplomacy-playing AIs.

Chenglei Si, Navita Goyal, Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, and Jordan Boyd-Graber. Large Language Models Help Humans Verify Truthfulness---Except When They Are Convincingly Wrong. North American Association for Computational Linguistics, 2024. [Bibtex]

@inproceedings{Si:Goyal:Wu:Zhao:Feng:III:Boyd-Graber-2024,
	Title = {Large Language Models Help Humans Verify Truthfulness---Except When They Are Convincingly Wrong},
	Author = {Chenglei Si and Navita Goyal and Tongshuang Wu and Chen Zhao and Shi Feng and Hal Daum\'{e} {III} and Jordan Boyd-Graber},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2024},
	Url = {http://cs.umd.edu/~jbg//docs/2024_naacl_convincingly.pdf},
}

IIS-2403436

Maharshi Gor, Hal Daumé III Tianyi Zhou, and Jordan Boyd-Graber. Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA. Empirical Methods in Natural Language Processing, 2024. [Talk] [Code] [Data] [Bibtex]
```
@inproceedings{Gor:Daume-III:Boyd-Graber-2024,
	Title = {Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA},
	Author = {Maharshi Gor and Hal {Daum\'{e} III} Tianyi Zhou and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2024},
	Location = {Miami},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_caimira.pdf},
}
```
Accessible Abstract: CAIMIRA discovers the skills that humans and AIs use to answer questions. By scraping websites where trivia nerds answer really difficult questions and posing those questions to AI models like GPT-4 and LLaMA-3-70B, while humans excel in knowledge-based abductive reasoning, AI outperforms on fact-based historical recall. This research suggests future challenges should focus on more complex reasoning and nuanced language tasks to better align AI development with human cognitive strengths.
Tasnim Kabir, Yoo Yeon Sung, Saptarashmi Bandyopadhyay, Hao Zou, Abhranil Chandra, and Jordan Lee Boyd-Graber. You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions. Empirical Methods in Natural Language Processing, 2024. [ArXiv] [Research Talk] [Code] [Bibtex]
```
@inproceedings{Kabir:Sung:Bandyopadhyay:Zou:Chandra:Boyd-Graber-2024,
	Title = {You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions},
	Author = {Tasnim Kabir and Yoo Yeon Sung and Saptarashmi Bandyopadhyay and Hao Zou and Abhranil Chandra and Jordan Lee Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Location = {Miami},
	Year = {2024},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_natural.pdf},
}
```
Accessible Abstract: Many of the questions for training AIs how to answer questions come from the queries users type into search engines (like Google's Natural Questions). Is there a cheaper---perhaps even better---way? We propose a "naturalization" technique to turn high-quality, rigorously edited trivia questions into examples that resembles Natural Questions. Training on our naturalized questions and testing on natural questions comes close to the results with using Natural Questions, and we can improve results on MMLU (a standard modern evaluation set) by using our data.

LORELEI

Daniel Peterson, Jordan Boyd-Graber, Martha Palmer, and Daisuke Kawahara. Leveraging VerbNet to build Corpus-Specific Verb Clusters. Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, 2016. [Bibtex]

@inproceedings{Peterson:Boyd-Graber:Palmer:Kawahara-2016,
	Title = {Leveraging {V}erb{N}et to build Corpus-Specific Verb Clusters},
	Author = {Daniel Peterson and Jordan Boyd-Graber and Martha Palmer and Daisuke Kawahara},
	Booktitle = {Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics},
	Year = {2016},
	Location = {Berlin, Germany},
	Url = {https://aclanthology.org/S16-2012/},
}

LORELEI

Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. Association for the Advancement of Artificial Intelligence, 2020. [Bibtex]

@inproceedings{Zhang:Fujinuma:Boyd-Graber-2020,
	Title = {Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification},
	Author = {Mozhi Zhang and Yoshinari Fujinuma and Jordan Boyd-Graber},
	Booktitle = {Association for the Advancement of Artificial Intelligence},
	Year = {2020},
	Location = {New York, New York},
	Url = {https://arxiv.org/abs/1812.09617},
}

Yoshinari Fujinuma, Michael Paul, and Jordan Boyd-Graber. A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity. Association for Computational Linguistics, 2019. [Bibtex]

@inproceedings{Fujinuma:Paul:Boyd-Graber-2019,
	Title = {A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity},
	Author = {Yoshinari Fujinuma and Michael Paul and Jordan Boyd-Graber},
	Booktitle = {Association for Computational Linguistics},
	Year = {2019},
	Location = {Florence, Italy},
	Url = {http://cs.umd.edu/~jbg//docs/2019_acl_modularity.pdf},
}

Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, and Jordan Boyd-Graber. Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. Association for Computational Linguistics, 2019. [Preprint] [Bibtex]

@inproceedings{Zhang:Xu:Kawarabayashi:Jegelka:Boyd-Graber-2019,
	Author = {Mozhi Zhang and Keyulu Xu and Ken-ichi Kawarabayashi and Stefanie Jegelka and Jordan Boyd-Graber},
	Title = {Are Girls Neko or Sh{\=o}jo? {C}ross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization},
	Booktitle = {Association for Computational Linguistics},
	Year = {2019},
	Location = {Florence, Italy},
	Url = {http://cs.umd.edu/~jbg//docs/2019_acl_clwe.pdf},
}

Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Multilingual Topic Model for Learning Weighted Topic Links Across Incomparable Corpora. Empirical Methods in Natural Language Processing, 2019. [Bibtex]

@inproceedings{Yang:Boyd-Graber:Resnik-2019,
	Title = {A Multilingual Topic Model for Learning Weighted Topic Links Across Incomparable Corpora},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2019},
	Location = {Hong Kong, China},
	Author = {Weiwei Yang and Jordan Boyd-Graber and Philip Resnik},
	Url = {http://cs.umd.edu/~jbg//docs/2019_emnlp_mtm.pdf},
}

Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. [Bibtex]

@inproceedings{Feng:Boyd-Graber-2019,
	Title = {What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play},
	Author = {Shi Feng and Jordan Boyd-Graber},
	Booktitle = {Intelligent User Interfaces},
	Year = {2019},
	Location = {Los Angeles, CA},
	Url = {http://cs.umd.edu/~jbg//docs/2019_iui_augment.pdf},
}

@inproceedings{Feng:Wallace:Boyd-Graber-2018,
	Title = {Interpreting Neural Networks with Nearest Neighbors},
	Author = {Shi Feng and Eric Wallace and Jordan Boyd-Graber},
	Booktitle = {EMNLP Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP},
	Year = {2018},
	Location = {Brussels, Belgium},
	Url = {http://aclweb.org/anthology/W18-5416},
}

@inproceedings{Feng:Wallace:II:Rodriguez:Iyyer:Boyd-Graber-2018,
	Title = {Pathologies of Neural Models Make Interpretation Difficult},
	Author = {Shi Feng and Eric Wallace and Alvin Grissom II and Pedro Rodriguez and Mohit Iyyer and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2018},
	Location = {Brussels, Belgium},
	Url = {http://cs.umd.edu/~jbg//docs/2018_emnlp_rs.pdf},
}

Michelle Yuan, Benjamin Van Durme, and Jordan Boyd-Graber. Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. Neural Information Processing Systems, 2018. [Code] [Bibtex]

@inproceedings{Yuan:Van-Durme:Boyd-Graber-2018,
	Title = {Multilingual Anchoring: Interactive Topic Modeling and Alignment  Across Languages},
	Author = {Michelle Yuan and Benjamin {Van Durme} and Jordan Boyd-Graber},
	Booktitle = {Neural Information Processing Systems},
	Year = {2018},
	Location = {Montreal, Quebec},
	Url = {http://cs.umd.edu/~jbg//docs/2018_neurips_mtanchor.pdf},
}

Shudong Hao, Michael J. Paul, and Jordan Boyd-Graber. Lessons from the Bible on Modern Topics: Multilingual Topic Model Evaluation on Low-Resource Languages. North American Association for Computational Linguistics, 2018. [Bibtex]

@inproceedings{Hao:Paul:Boyd-Graber-2018,
	Title = {Lessons from the Bible on Modern Topics: Multilingual Topic Model Evaluation on Low-Resource Languages},
	Author = {Shudong Hao and Michael J. Paul and Jordan Boyd-Graber},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2018},
	Location = {New Orleans, LA},
	Url = {http://cs.umd.edu/~jbg//docs/2018_naacl_mltm_eval.pdf},
}

Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. [Bibtex]

@inproceedings{Yang:Boyd-Graber:Resnik-2017,
	Title = {Adapting Topic Models using Lexical Associations with Tree Priors},
	Author = {Weiwei Yang and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2017},
	Location = {Copenhagen, Denmark},
	Url = {http://cs.umd.edu/~jbg//docs/2017_emnlp_tree_prior.pdf},
}

Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. [Supplement] [Bibtex]

@inproceedings{Yang:Boyd-Graber:Resnik-2016,
	Title = {A Discriminative Topic Model using Document Network Structure},
	Author = {Weiwei Yang and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Association for Computational Linguistics},
	Year = {2016},
	Location = {Berlin, Brandenburg},
	Url = {http://cs.umd.edu/~jbg//docs/2016_acl_docblock.pdf},
}

Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2016. [Bibtex]

@inproceedings{Yang:Boyd-Graber:Resnik-2016,
	Title = {Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors},
	Author = {Weiwei Yang and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Mid-Atlantic Student Colloquium on Speech, Language, and Learning},
	Year = {2016},
	Location = {Philadephia},
}

Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors. Empirical Methods in Natural Language Processing, 2015. [Bibtex]

@inproceedings{Yang:Boyd-Graber:Resnik-2015,
	Title = {Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors},
	Url = {http://cs.umd.edu/~jbg//docs/2015_emnlp_hinge_link.pdf},
	Author = {Weiwei Yang and Jordan Boyd-Graber and Philip Resnik},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2015},
	Location = {Lisbon, Portugal},
}

NIST

Ishani Mondal, Shwetha S, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Boyd-Graber. Presentations by the People, for the People: Harnessing LLMs for Generating Persona-Aware Slides from Documents. European Association for Computational Linguistics, 2024. [Bibtex]

@inproceedings{Mondal:S:Natarajan:Garimella:Bandyopadhyay:Boyd-Graber-2024,
	Title = {Presentations by the People, for the People: Harnessing LLMs for Generating Persona-Aware Slides from Documents},
	Author = {Ishani Mondal and Shwetha S and Anandhavelu Natarajan and Aparna Garimella and Sambaran Bandyopadhyay and Jordan Boyd-Graber},
	Booktitle = {European Association for Computational Linguistics},
	Year = {2024},
	Url = {http://cs.umd.edu/~jbg//docs/2024_eacl_slides.pdf},
}

Zongxia Li, Andrew Mao, Daniel Kofi Stephens, Pranav Goel, Emily Walpole, Juan Francisco Fung, Alden Dima, and Jordan Lee Boyd-Graber. TENOR: Topic Enabled Neural Organization and Recommendation: Evaluating Topic Models in Task Based Settings. European Association for Computational Linguistics, 2024. [Bibtex]

@inproceedings{Li:Mao:Stephens:Goel:Walpole:Fung:Dima:Boyd-Graber-2024,
	Title = {TENOR: Topic Enabled Neural Organization and Recommendation: Evaluating Topic Models in Task Based Settings},
	Author = {Zongxia Li and Andrew Mao and Daniel Kofi Stephens and Pranav Goel and Emily Walpole and Juan Francisco Fung and Alden Dima and Jordan Lee Boyd-Graber},
	Booktitle = {European Association for Computational Linguistics},
	Year = {2024},
	Url = {http://cs.umd.edu/~jbg//docs/2024_eacl_tenor.pdf},
}

Personalization

Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, and Jordan Boyd-Graber. A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. Empirical Methods in Natural Language Processing, 2024. [Code and Data] [Research Talk] [Bibtex]
```
@inproceedings{Balepur:Shu:Hoyle:Robey:Feng:Goldfarb-Tarrant:Boyd-Graber-2024,
	Title = {A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick},
	Author = {Nishant Balepur and Matthew Shu and Alexander Hoyle and Alison Robey and Shi Feng and Seraphina Goldfarb-Tarrant and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2024},
	Location = {Miami},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_mnemonic.pdf},
}
```
Accessible Abstract: Learning vocabulary (e.g., benevolent) can be tedious, but using mnemonics (e.g., benevolent sounds like "benefits," and a kind boss gives benefits) makes it more engaging and effective. This paper introduces SMART, a large language model trained to produce mnemonics based on feedback from flashcard learners. Students struggle to predict which mnemonics will help them most. Still, by training SMART on both student preferences and learning outcomes, we can generate mnemonics as effectively as GPT-4, but at a much lower cost.
Maharshi Gor, Hal Daumé III Tianyi Zhou, and Jordan Boyd-Graber. Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA. Empirical Methods in Natural Language Processing, 2024. [Talk] [Code] [Data] [Bibtex]
```
@inproceedings{Gor:Daume-III:Boyd-Graber-2024,
	Title = {Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA},
	Author = {Maharshi Gor and Hal {Daum\'{e} III} Tianyi Zhou and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2024},
	Location = {Miami},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_caimira.pdf},
}
```
Accessible Abstract: CAIMIRA discovers the skills that humans and AIs use to answer questions. By scraping websites where trivia nerds answer really difficult questions and posing those questions to AI models like GPT-4 and LLaMA-3-70B, while humans excel in knowledge-based abductive reasoning, AI outperforms on fact-based historical recall. This research suggests future challenges should focus on more complex reasoning and nuanced language tasks to better align AI development with human cognitive strengths.
Tasnim Kabir, Yoo Yeon Sung, Saptarashmi Bandyopadhyay, Hao Zou, Abhranil Chandra, and Jordan Lee Boyd-Graber. You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions. Empirical Methods in Natural Language Processing, 2024. [ArXiv] [Research Talk] [Code] [Bibtex]
```
@inproceedings{Kabir:Sung:Bandyopadhyay:Zou:Chandra:Boyd-Graber-2024,
	Title = {You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions},
	Author = {Tasnim Kabir and Yoo Yeon Sung and Saptarashmi Bandyopadhyay and Hao Zou and Abhranil Chandra and Jordan Lee Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Location = {Miami},
	Year = {2024},
	Url = {http://cs.umd.edu/~jbg//docs/2024_emnlp_natural.pdf},
}
```
Accessible Abstract: Many of the questions for training AIs how to answer questions come from the queries users type into search engines (like Google's Natural Questions). Is there a cheaper---perhaps even better---way? We propose a "naturalization" technique to turn high-quality, rigorously edited trivia questions into examples that resembles Natural Questions. Training on our naturalized questions and testing on natural questions comes close to the results with using Natural Questions, and we can improve results on MMLU (a standard modern evaluation set) by using our data.

Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, and Dinesh Manocha. AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models. Findings of the Empirical Methods in Natural Language Processing, 2024. [Bibtex]

@article{Wu:Guan:Li:Huang:Liu:Wang:Xian:Shrivastava:Huang:Boyd-Graber:Zhou:Manocha-2024,
	Title = {AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models},
	Author = {Xiyang Wu and Tianrui Guan and Dianqi Li and Shuaiyi Huang and Xiaoyu Liu and Xijun Wang and Ruiqi Xian and Abhinav Shrivastava and Furong Huang and Jordan Boyd-Graber and Tianyi Zhou and Dinesh Manocha},
	Journal = {Findings of the Empirical Methods in Natural Language Processing},
	Year = {2024},
	Location = {Miami},
	Url = {https://arxiv.org/abs/2406.10900},
}

Zongxia Li, Ishani Mondal, Huy Nghiem, Yijun Liang, and Jordan Boyd-Graber. PEDANTS (Precise Evaluations of Diverse Answer Nominee Text for Skinflints): Use Evaluation Metrics Wisely---Efficient Evaluation Analysis and Benchmarking for Open-Domain Question Answering. Findings of the Empirical Methods in Natural Language Processing, 2024. [Bibtex]

@article{Li:Mondal:Nghiem:Liang:Boyd-Graber-2024,
	Title = {PEDANTS (Precise Evaluations of Diverse Answer Nominee Text for Skinflints): Use Evaluation Metrics Wisely---Efficient Evaluation Analysis and Benchmarking for Open-Domain Question Answering},
	Author = {Zongxia Li and Ishani Mondal and Huy Nghiem and Yijun Liang and Jordan Boyd-Graber},
	Journal = {Findings of the Empirical Methods in Natural Language Processing},
	Location = {Miami},
	Year = {2024},
	Url = {https://arxiv.org/abs/2402.11161},
}

Rosie

Neha Punklik Srikanth, Rupak Sarkar, Mane, Heran Y., Aparicio, Elizabeth M., Nguyen, Quynh C., Rachel Rudinger, and Jordan Boyd-Graber. Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering. North American Association for Computational Linguistics, 2024. [Code and Data] [Bibtex]

@inproceedings{Srikanth:Sarkar:Y.:M.:C.:Rudinger:Boyd-Graber-2024,
	Title = {Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering},
	Author = {Neha Srikanth and Rupak Sarkar and Mane, Heran Y. and Aparicio, Elizabeth M. and Nguyen, Quynh C. and Rachel Rudinger and Jordan Boyd-Graber},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2024},
	Url = {http://cs.umd.edu/~jbg//docs/2024_naacl_pregnant.pdf},
}

Rosie

Quynh C. Nguyen, Elizabeth M. Aparicio, Michelle Jasczynski, Amara Channell Doig, Xiaohe Yue, Heran Mane, Neha Punklik Srikanth, Francia Ximena Marin Gutierrez, Nataly Delcid, Xin He, and Jordan Boyd-Graber. Randomized Pilot of Rosie, a Health Education Question-and-Answer Chatbot for New Mothers. Journal of Medical Internet Research: Journal of Formative Research, 2024. [Bibtex]

@article{Nguyen:Aparicio:Jasczynski:Doig:Yue:Mane:Srikanth:Gutierrez:Delcid:He:Boyd-Graber-2024,
	Title = {Randomized Pilot of Rosie, a Health Education Question-and-Answer Chatbot for New Mothers},
	Author = {Quynh C. Nguyen and  Elizabeth M. Aparicio and  Michelle Jasczynski and  Amara Channell Doig and  Xiaohe Yue and  Heran Mane and Neha Srikanth and Francia Ximena Marin Gutierrez and Nataly Delcid and  Xin He and Jordan Boyd-Graber},
	Journal = {Journal of Medical Internet Research: Journal of Formative Research},
	Year = {2024},
	Url = {https://formative.jmir.org/2024/1/e51361},
}

Mane, Heran Y., Channell Doig, Amara, Marin Gutierrez, Francia Ximena, Jasczynski, Michelle, Yue, Xiaohe, Neha Punklik Srikanth, Mane, Sourabh, Sun, Abby, Moats, Rachel Ann, Patel, Pragat, He, Xin, Jordan Boyd-Graber, Aparicio, Elizabeth M., and Nguyen, Quynh C.. Practical Guidance for the Development of Rosie, a Health Education Question-and-Answer Chatbot for New Mothers. Journal of Public Health Management and Practice, 2023. [Bibtex]

@article{Y.:Amara:Ximena:Michelle:Xiaohe:Srikanth:Sourabh:Abby:Ann:Pragat:Xin:Boyd-Graber:M.:C.-2023,
	Title = {Practical Guidance for the Development of Rosie, a Health Education Question-and-Answer Chatbot for New Mothers},
	Author = {Mane, Heran Y. and Channell Doig, Amara and Marin Gutierrez, Francia Ximena and Jasczynski, Michelle and Yue, Xiaohe and Neha Srikanth and Mane, Sourabh and Sun, Abby and Moats, Rachel Ann and Patel, Pragat and He, Xin and Jordan Boyd-Graber and Aparicio, Elizabeth M. and Nguyen, Quynh C.},
	Journal = {Journal of Public Health Management and Practice},
	Year = {2023},
	Url = {https://journals.lww.com/jphmp/fulltext/2023/09000/practical_guidance_for_the_development_of_rosie,_a.9.aspx},
}

SHADE

Wichayaporn Wongkamjan and Feng Gu and Yanze Wang and Ulf Hermjakob and Jonathan May and Brandon M. Stewart and Jonathan K. Kummerfeld and Denis Peskoff and Jordan Lee Boyd-Graber. More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play. Association for Computational Linguistics, 2024. [Bibtex]
```
@inproceedings{Boyd-Graber-2024,
	Title = {More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play},
	Booktitle = {Association for Computational Linguistics},
	Year = {2024},
	Location = {Bangkok, Thailand},
	Url = {http://cs.umd.edu/~jbg//docs/2024_acl_cicero.pdf},
}
```
Accessible Abstract: Meta's recent AI, Cicero, grabbed headlines by its ability to beat humans at the game of Diplomacy: notable because players of the game not just need to make the right moves but also need to negotiate with each other in natural language. This paper investigates why it wins so many games, measuring its ability to persuade and trick other players. While Cicero wins just about every game, this is because of superhuman strategy, not superhuman communication, suggesting there is still further room for developing Diplomacy-playing AIs.

Scaling Insight

Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei, and James A. Evans. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academies of Science, 2018. [Journal] [Bibtex]

@article{Gerow:Hu:Boyd-Graber:Blei:Evans-2018,
	Title = {Measuring Discursive Influence Across Scholarship},
	Author = {Aaron Gerow and Yuening Hu and Jordan Boyd-Graber and David M. Blei and James A. Evans},
	Journal = {Proceedings of the National Academies of Science},
	Year = {2018},
}

You Lu, Jeff Lund, and Jordan Boyd-Graber. Why ADAGRAD Fails for Online Topic Modeling. Empirical Methods in Natural Language Processing, 2017. [Bibtex]

@inproceedings{Lu:Lund:Boyd-Graber-2017,
	Title = {Why ADAGRAD Fails for Online Topic Modeling},
	Author = {You Lu and Jeff Lund and Jordan Boyd-Graber},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Url = {http://cs.umd.edu/~jbg//docs/2017_emnlp_adagrad_olda.pdf},
	Year = {2017},
	Location = {Copenhagen, Denmark},
}

@inproceedings{Poursabzi-Sangdeh:Boyd-Graber:Findlater:Seppi-2016,
	Title = {ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling},
	Author = {Forough Poursabzi-Sangdeh and Jordan Boyd-Graber and Leah Findlater and Kevin Seppi},
	Booktitle = {Association for Computational Linguistics},
	Year = {2016},
	Location = {Berlin, Brandenburg},
	Url = {http://cs.umd.edu/~jbg//docs/2016_acl_doclabel.pdf},
}

Evgeny Klochikhin and Jordan Boyd-Graber. Text Analysis. Big Data and Social Science Research: Theory and Practical Approaches, 2016. [Bibtex]

@inbook{Klochikhin:Boyd-Graber-2016,
	Editor = {Julia Lane and Ian Foster and Rayid Ghani and Ron Jarmin and Frauke Kreuter},
	Title = {Text Analysis},
	Author = {Evgeny Klochikhin and Jordan Boyd-Graber},
	Booktitle = {Big Data and Social Science Research: Theory and Practical Approaches},
	Publisher = {Taylor Francis},
	Year = {2016},
}

Forough Poursabzi-Sangdeh and Jordan Boyd-Graber. Speeding Document Annotation with Topic Models. NAACL Student Research Workshop, 2015. [Bibtex]

@inproceedings{Poursabzi-Sangdeh:Boyd-Graber-2015,
	Title = {Speeding Document Annotation with Topic Models},
	Author = {Forough Poursabzi-Sangdeh and Jordan Boyd-Graber},
	Booktitle = {NAACL Student Research Workshop},
	Year = {2015},
	Location = {Denver, CO},
}

Thinking on Your Feet

Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daumé III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. [Code/Data] [Bibtex]

@inproceedings{Iyyer:Manjunatha:Guha:Vyas:Boyd-Graber:Daume-III:Davis-2017,
	Title = {The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives},
	Author = {Mohit Iyyer and Varun Manjunatha and Anupam Guha and Yogarshi Vyas and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Larry Davis},
	Booktitle = {Computer Vision and Pattern Recognition},
	Year = {2017},
	Url = {http://cs.umd.edu/~jbg//docs/2017_cvpr_comics.pdf},
}

Khanh Nguyen, Jordan Boyd-Graber, and Hal Daumé III. Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback. Empirical Methods in Natural Language Processing, 2017. [Code] [Bibtex]

@inproceedings{Nguyen:Boyd-Graber:Daume-III-2017,
	Title = {Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback},
	Author = {Khanh Nguyen and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2017},
	Location = {Copenhagen, Denmark},
	Url = {http://cs.umd.edu/~jbg//docs/2017_emnlp_bandit_mt.pdf},
}

Alvin Grissom II, Naho Orita, and Jordan Boyd-Graber. Incremental Prediction of Sentence-final Verbs. Conference on Computational Natural Language Learning, 2016. [Bibtex]

@inproceedings{Grissom-II:Orita:Boyd-Graber-2016,
	Title = {Incremental Prediction of Sentence-final Verbs},
	Author = {Alvin {Grissom II} and Naho Orita and Jordan Boyd-Graber},
	Booktitle = {Conference on Computational Natural Language Learning},
	Year = {2016},
	Location = {Berlin, Germany},
	Url = {http://cs.umd.edu/~jbg//docs/2016_conll_verbpred.pdf},
}

He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. [Video] [Bibtex]

@inproceedings{He:Boyd-Graber:Kwok:Daume-III-2016,
	Title = {Opponent Modeling in Deep Reinforcement Learning},
	Author = {He He and Jordan Boyd-Graber and Kevin Kwok and Hal {Daum\'{e} III}},
	Booktitle = {International Conference on Machine Learning},
	Year = {2016},
	Location = {New York, NY},
	Url = {http://cs.umd.edu/~jbg//docs/2016_icml_opponent.pdf},
}

Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016. [Data] [Bibtex]

@inproceedings{Guha:Iyyer:Boyd-Graber-2016,
	Title = {A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions},
	Author = {Anupam Guha and Mohit Iyyer and Jordan Boyd-Graber},
	Booktitle = {NAACL Human-Computer Question Answering Workshop},
	Year = {2016},
	Location = {San Diego, CA},
	Url = {http://cs.umd.edu/~jbg//docs/2016_naacl_paintings.pdf},
}

Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daumé III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. [Code/Data] [Bibtex]

@inproceedings{Iyyer:Guha:Chaturvedi:Boyd-Graber:Daume-III-2016,
	Title = {Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships},
	Author = {Mohit Iyyer and Anupam Guha and Snigdha Chaturvedi and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2016},
	Location = {San Diego, CA},
	Url = {http://cs.umd.edu/~jbg//docs/2016_naacl_relationships.pdf},
}

Best paper award (2 out of 1592)

He He, Jordan Boyd-Graber, and Hal Daumé III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. [Talk] [Bibtex]

@inproceedings{He:Boyd-Graber:Daume-III-2016,
	Title = {Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation},
	Author = {He He and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2016},
	Location = {San Diego, CA},
	Url = {http://cs.umd.edu/~jbg//docs/2016_naacl_interpretese.pdf},
}

Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. [Slides] [Code] [Talk] [Bibtex]

@inproceedings{Iyyer:Manjunatha:Boyd-Graber:Daume-III-2015,
	Title = {Deep Unordered Composition Rivals Syntactic Methods for Text Classification},
	Author = {Mohit Iyyer and Varun Manjunatha and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {Association for Computational Linguistics},
	Year = {2015},
	Location = {Beijing, China},
	Url = {http://cs.umd.edu/~jbg//docs/2015_acl_dan.pdf},
}

Vlad Niculae, Srijan Kumar, Jordan Boyd-Graber, and Cristian Danescu-Niculescu-Mizil. Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game. Association for Computational Linguistics, 2015. [Code/Data] [Bibtex]
```
@inproceedings{Niculae:Kumar:Boyd-Graber:Danescu-Niculescu-Mizil-2015,
	Title = {Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game},
	Author = {Vlad Niculae and Srijan Kumar and Jordan Boyd-Graber and Cristian Danescu-Niculescu-Mizil},
	Booktitle = {Association for Computational Linguistics},
	Year = {2015},
	Location = {Beijing, China},
	Url = {http://cs.umd.edu/~jbg//docs/2015_acl_diplomacy.pdf},
}
```
Accessible Abstract: This paper introduces the application of natural language processing techniques to understand the relationships (and their dissolution) in the game of Diplomacy. This popular board game simulates Europe at the eve of World War I and forces players to work with each other to forge alliances and make plans together. However, the game's setup also encourages players to turn against each other. This paper analyzes whether we can predict these betrayals (we can!) and the linguistic and social phenomena (demands, politeness, and planning) that can predict when a betrayal will happen.

He He, Alvin Grissom II, Jordan Boyd-Graber, and Hal Daumé III. Syntax-based Rewriting for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2015. [Bibtex]

@inproceedings{He:Grissom-II:Boyd-Graber:Daume-III-2015,
	Title = {Syntax-based Rewriting for Simultaneous Machine Translation},
	Author = {He He and Alvin {Grissom II} and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2015},
	Location = {Lisbon, Portugal},
	Url = {http://cs.umd.edu/~jbg//docs/2015_emnlp_rewrite.pdf},
}

Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daumé III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015. [Bibtex]

@inproceedings{Boyd-Graber:Iyyer:He:Daume-III-2015,
	Title = {Interactive Incremental Question Answering},
	Author = {Jordan Boyd-Graber and Mohit Iyyer and He He and Hal {Daum\'{e} III}},
	Booktitle = {Neural Information Processing Systems},
	Year = {2015},
	Location = {Montreal, Canada},
}

This won the best demonstration award at NIPS 2015

Anupam Guha, Mohit Iyyer, Danny Bouman, and Jordan Boyd-Graber. Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers. North American Association for Computational Linguistics, 2015. [Code/Data] [Slides] [Video] [LaTeX] [Bibtex]

@inproceedings{Guha:Iyyer:Bouman:Boyd-Graber-2015,
	Title = {Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers},
	Author = {Anupam Guha and Mohit Iyyer and Danny Bouman and Jordan Boyd-Graber},
	Booktitle = {North American Association for Computational Linguistics},
	Year = {2015},
	Location = {Denver, Colorado},
	Url = {http://cs.umd.edu/~jbg//docs/2015_naacl_qb_coref.pdf},
}

Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daumé III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. [Code/Data] [Bibtex]

@inproceedings{Iyyer:Boyd-Graber:Claudino:Socher:Daume-III-2014,
	Title = {A Neural Network for Factoid Question Answering over Paragraphs},
	Author = {Mohit Iyyer and Jordan Boyd-Graber and Leonardo Claudino and Richard Socher and Hal {Daum\'{e} III}},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2014},
	Location = {Doha, Qatar},
	Url = {http://cs.umd.edu/~jbg//docs/2014_emnlp_qb_rnn.pdf},
}

The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7.

Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daumé III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. [Talk] [Bibtex]

@inproceedings{Grissom-II:He:Boyd-Graber:Morgan:Daume-III-2014,
	Title = {Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation},
	Author = {Alvin {Grissom II} and He He and Jordan Boyd-Graber and John Morgan and Hal {Daum\'{e} III}},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2014},
	Location = {Doha, Qatar},
	Url = {http://cs.umd.edu/~jbg//docs/2014_emnlp_simtrans.pdf},
}

Mohit Iyyer, Jordan Boyd-Graber, and Hal Daumé III. Generating Sentences from Semantic Vector Space Representations. NIPS Workshop on Learning Semantics, 2014. [Bibtex]

@inproceedings{Iyyer:Boyd-Graber:Daume-III-2014,
	Title = {Generating Sentences from Semantic Vector Space Representations},
	Author = {Mohit Iyyer and Jordan Boyd-Graber and Hal {Daum\'{e} III}},
	Booktitle = {NIPS Workshop on Learning Semantics},
	Year = {2014},
	Location = {Montreal, Canada},
}

Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daumé III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. [Presentation] [Data] [Bibtex]

@inproceedings{Boyd-Graber:Satinoff:He:Daume-III-2012,
	Title = {Besting the Quiz Master: Crowdsourcing Incremental Classification Games},
	Author = {Jordan Boyd-Graber and Brianna Satinoff and He He and Hal {Daum\'{e} III}},
	Booktitle = {Empirical Methods in Natural Language Processing},
	Year = {2012},
	Url = {http://cs.umd.edu/~jbg//docs/qb_emnlp_2012.pdf},
	Location = {Jeju, South Korea},
}

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