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arXiv · 1909.01013

Duality Regularization for Unsupervised Bilingual Lexicon Induction

Xuefeng Bai·Yue Zhang·Hailong Cao·Tiejun Zhao
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Abstract

Unsupervised bilingual lexicon induction naturally exhibits duality, which results from symmetry in back-translation. For example, EN-IT and IT-EN induction can be mutually primal and dual problems. Current state-of-the-art methods, however, consider the two tasks independently. In this paper, we propose to train primal and dual models jointly, using regularizers to encourage consistency in back translation cycles. Experiments across 6 language pairs show that the proposed method significantly outperforms competitive baselines, obtaining the best-published results on a standard benchmark.

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Xuefeng Bai, Yue Zhang, Hailong Cao, Tiejun Zhao. 2019-09-03. Duality Regularization for Unsupervised Bilingual Lexicon Induction. https://arxiv.org/abs/1909.01013

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