Adversarial Training for Unsupervised Bilingual Lexicon Induction

Meng Zhang, Yang Liu, Huanbo Luan, Maosong Sun


Abstract
Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.
Anthology ID:
P17-1179
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1959–1970
Language:
URL:
https://aclanthology.org/P17-1179
DOI:
10.18653/v1/P17-1179
Bibkey:
Cite (ACL):
Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. 2017. Adversarial Training for Unsupervised Bilingual Lexicon Induction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1959–1970, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Adversarial Training for Unsupervised Bilingual Lexicon Induction (Zhang et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1179.pdf
Software:
 P17-1179.Software.zip
Poster:
 P17-1179.Poster.pdf