GRACE (BBN): Low Resource Languages for Emergent Incidents (LORELEI)

Project funded by DARPA (LORELEI)
PI: Philip Resnik
coPI (PI while at Colorado): Jordan Boyd-Graber

The goal of the Low Resource Languages for Emergent Incidents (LORELEI) Program is to dramatically advance the state of computational linguistics and human language technology to enable rapid, low-cost development of capabilities for low-resource languages. With the understanding that even with perfect translation, there would still be too much material for analysts to use effectively, LORELEI research will not be focused solely on machine translation. While LORELEI technologies may include partial or fully automated speech recognition and/or machine translation, the overall goal will not be translating foreign language material into English but providing situational awareness by identifying elements of information in foreign language and English sources, such as topics, names, events, sentiment and relationships.

The UMD focused on improving representations for low resource langauges through using related languages and human interaction.

	Jordan Boyd-Graber Assistant Professor, Computer Science (UMD)
	Shi Feng PhD Student, Computer Science (UMD)
	Ting Hua Postdoc (JHU)
	Yoshinari Fujinuma PhD Student, Computer Science (Colorado)
	Weiwei Yang PhD Student, Computer Science (UMD)
	Mozhi Zhang PhD Student, Computer Science (UMD)

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• Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka. What Can Neural Networks Reason About? ICLR, 2020. ~~Pubs:LORELEI~~

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the researchers and do not necessarily reflect the views of the sponsor.