TY - GEN
T1 - Learning translations via matrix completion
AU - Wijaya, Derry
AU - Callahan, Brendan
AU - Hewitt, John
AU - Gao, Jie
AU - Ling, Xiao
AU - Apidianaki, Marianna
AU - Callison-Burch, Chris
N1 - Funding Information:
This material is based in part on research sponsored by DARPA under grant number HR0011-15-C-0115 (the LORELEI program). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA and the U.S. Government. This work was also supported by the French National Research Agency under project ANR-16-CE33-0013, and by Amazon through the Amazon Academic Research Awards (AARA) program.
Funding Information:
This material is based in part on research sponsored by DARPA under grant number HR0011-15-C-0115 (the LORELEI program). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA and the U.S. Government.
Funding Information:
This work was also supported by the French National Research Agency under project ANR-16-CE33-0013, and by Amazon through the Amazon Academic Research Awards (AARA) program.
Publisher Copyright:
© 2017 Association for Computational Linguistics.
PY - 2017
Y1 - 2017
N2 - Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.
AB - Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.
UR - http://www.scopus.com/inward/record.url?scp=85063085507&partnerID=8YFLogxK
U2 - 10.18653/v1/d17-1152
DO - 10.18653/v1/d17-1152
M3 - Conference Paper
AN - SCOPUS:85063085507
SP - 1452
EP - 1463
BT - EMNLP 2017 - The Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
A2 - Hwa, Rebecca
A2 - Riedel, Sebastian
PB - Association for Computational Linguistics (ACL)
CY - Stroudsburg PA USA
T2 - Empirical Methods in Natural Language Processing 2017
Y2 - 9 September 2017 through 11 September 2017
ER -
