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        <title>Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations</title>
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    <name type="personal">
        <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Mirella</namePart>
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            <namePart type="given">Phil</namePart>
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    <abstract>We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model.</abstract>
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%0 Conference Proceedings
%T Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations
%A Heyman, Geert
%A Vulić, Ivan
%A Moens, Marie-Francine
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F heyman-etal-2017-bilingual
%X We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model.
%U https://aclanthology.org/E17-1102
%P 1085-1095

Markdown (Informal)

[Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations](https://aclanthology.org/E17-1102) (Heyman et al., EACL 2017)

• Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations (Heyman et al., EACL 2017)

ACL

• Geert Heyman, Ivan Vulić, and Marie-Francine Moens. 2017. Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1085–1095, Valencia, Spain. Association for Computational Linguistics.