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        <title>A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction</title>
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        <namePart type="given">Yanyang</namePart>
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    <name type="personal">
        <namePart type="given">Yingfeng</namePart>
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    <name type="personal">
        <namePart type="given">Huizhen</namePart>
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    <name type="personal">
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction
%A Li, Yanyang
%A Luo, Yingfeng
%A Lin, Ye
%A Du, Quan
%A Wang, Huizhen
%A Huang, Shujian
%A Xiao, Tong
%A Zhu, Jingbo
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 dec
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F li-etal-2020-simple
%X 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.
%R 10.18653/v1/2020.coling-main.526
%U https://aclanthology.org/2020.coling-main.526
%U https://doi.org/10.18653/v1/2020.coling-main.526
%P 5990-6001

Markdown (Informal)

[A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction](https://aclanthology.org/2020.coling-main.526) (Li et al., COLING 2020)

• A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (Li et al., COLING 2020)

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.