A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction

Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, Jingbo Zhu


Abstract
Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese. In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed. We propose Iterative Dimension Reduction to bridge this gap. Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13.64 55.53% between English and four distant languages, i.e., Chinese, Japanese, Vietnamese and Thai.
Anthology ID:
2020.coling-main.526
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5990–6001
Language:
URL:
https://aclanthology.org/2020.coling-main.526
DOI:
10.18653/v1/2020.coling-main.526
Bibkey:
Cite (ACL):
Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, Shujian Huang, Tong Xiao, and Jingbo Zhu. 2020. A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5990–6001, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (Li et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/update-css-js/2020.coling-main.526.pdf