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        <title>A Simple Approach to Learning Unsupervised Multilingual Embeddings</title>
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        <namePart type="given">Pratik</namePart>
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        <namePart type="given">Mayank</namePart>
        <namePart type="family">Meghwanshi</namePart>
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    <name type="personal">
        <namePart type="given">Bamdev</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
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            <namePart type="given">Trevor</namePart>
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    <abstract>Recent progress on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embedding space for several languages. A popular framework to solve the latter problem is to solve the following two sub-problems jointly: 1) learning unsupervised word alignment between several language pairs, and 2) learning how to map the monolingual embeddings of every language to shared multilingual space. In contrast, we propose a simple approach by decoupling the above two sub-problems and solving them separately, one after another, using existing techniques. We show that this proposed approach obtains surprisingly good performance in tasks such as bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing. When distant languages are involved, the proposed approach shows robust behavior and outperforms existing unsupervised multilingual word embedding approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.240</identifier>
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%0 Conference Proceedings
%T A Simple Approach to Learning Unsupervised Multilingual Embeddings
%A Jawanpuria, Pratik
%A Meghwanshi, Mayank
%A Mishra, Bamdev
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jawanpuria-etal-2020-simple
%X Recent progress on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embedding space for several languages. A popular framework to solve the latter problem is to solve the following two sub-problems jointly: 1) learning unsupervised word alignment between several language pairs, and 2) learning how to map the monolingual embeddings of every language to shared multilingual space. In contrast, we propose a simple approach by decoupling the above two sub-problems and solving them separately, one after another, using existing techniques. We show that this proposed approach obtains surprisingly good performance in tasks such as bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing. When distant languages are involved, the proposed approach shows robust behavior and outperforms existing unsupervised multilingual word embedding approaches.
%R 10.18653/v1/2020.emnlp-main.240
%U https://aclanthology.org/2020.emnlp-main.240/
%U https://doi.org/10.18653/v1/2020.emnlp-main.240
%P 2995-3001

Markdown (Informal)

[A Simple Approach to Learning Unsupervised Multilingual Embeddings](https://aclanthology.org/2020.emnlp-main.240/) (Jawanpuria et al., EMNLP 2020)

• A Simple Approach to Learning Unsupervised Multilingual Embeddings (Jawanpuria et al., EMNLP 2020)

ACL

• Pratik Jawanpuria, Mayank Meghwanshi, and Bamdev Mishra. 2020. A Simple Approach to Learning Unsupervised Multilingual Embeddings. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2995–3001, Online. Association for Computational Linguistics.