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    <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.</abstract>
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%0 Conference Proceedings
%T Adversarial Training for Unsupervised Bilingual Lexicon Induction
%A Zhang, Meng
%A Liu, Yang
%A Luan, Huanbo
%A Sun, Maosong
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F zhang-etal-2017-adversarial
%X 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.
%R 10.18653/v1/P17-1179
%U https://aclanthology.org/P17-1179
%U https://doi.org/10.18653/v1/P17-1179
%P 1959-1970

Markdown (Informal)

[Adversarial Training for Unsupervised Bilingual Lexicon Induction](https://aclanthology.org/P17-1179) (Zhang et al., ACL 2017)

• Adversarial Training for Unsupervised Bilingual Lexicon Induction (Zhang et al., ACL 2017)

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.