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        <title>Learning Unsupervised Word Translations Without Adversaries</title>
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    <name type="personal">
        <namePart type="given">Tanmoy</namePart>
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    <name type="personal">
        <namePart type="given">Makoto</namePart>
        <namePart type="family">Yamada</namePart>
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    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Hospedales</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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    <abstract>Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised – no seed dictionary or parallel corpora required; and introduces no adversary – therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1063</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Learning Unsupervised Word Translations Without Adversaries
%A Mukherjee, Tanmoy
%A Yamada, Makoto
%A Hospedales, Timothy
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F mukherjee-etal-2018-learning
%X Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised – no seed dictionary or parallel corpora required; and introduces no adversary – therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.
%R 10.18653/v1/D18-1063
%U https://aclanthology.org/D18-1063
%U https://doi.org/10.18653/v1/D18-1063
%P 627-632

Markdown (Informal)

[Learning Unsupervised Word Translations Without Adversaries](https://aclanthology.org/D18-1063) (Mukherjee et al., EMNLP 2018)

• Learning Unsupervised Word Translations Without Adversaries (Mukherjee et al., EMNLP 2018)

ACL

• Tanmoy Mukherjee, Makoto Yamada, and Timothy Hospedales. 2018. Learning Unsupervised Word Translations Without Adversaries. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 627–632, Brussels, Belgium. Association for Computational Linguistics.