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        <title>Multilingual word translation using auxiliary languages</title>
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        <namePart type="given">Hagai</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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            <namePart type="given">Jing</namePart>
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    <abstract>Current multilingual word translation methods are focused on jointly learning mappings from each language to a shared space. The actual translation, however, is still performed as an isolated bilingual task. In this study we propose a multilingual translation procedure that uses all the learned mappings to translate a word from one language to another. For each source word, we first search for the most relevant auxiliary languages. We then use the translations to these languages to form an improved representation of the source word. Finally, this representation is used for the actual translation to the target language. Experiments on a standard multilingual word translation benchmark demonstrate that our model outperforms state of the art results.</abstract>
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%0 Conference Proceedings
%T Multilingual word translation using auxiliary languages
%A Taitelbaum, Hagai
%A Chechik, Gal
%A Goldberger, Jacob
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F taitelbaum-etal-2019-multilingual
%X Current multilingual word translation methods are focused on jointly learning mappings from each language to a shared space. The actual translation, however, is still performed as an isolated bilingual task. In this study we propose a multilingual translation procedure that uses all the learned mappings to translate a word from one language to another. For each source word, we first search for the most relevant auxiliary languages. We then use the translations to these languages to form an improved representation of the source word. Finally, this representation is used for the actual translation to the target language. Experiments on a standard multilingual word translation benchmark demonstrate that our model outperforms state of the art results.
%R 10.18653/v1/D19-1134
%U https://aclanthology.org/D19-1134
%U https://doi.org/10.18653/v1/D19-1134
%P 1330-1335

Markdown (Informal)

[Multilingual word translation using auxiliary languages](https://aclanthology.org/D19-1134) (Taitelbaum et al., EMNLP-IJCNLP 2019)

• Multilingual word translation using auxiliary languages (Taitelbaum et al., EMNLP-IJCNLP 2019)

ACL

• Hagai Taitelbaum, Gal Chechik, and Jacob Goldberger. 2019. Multilingual word translation using auxiliary languages. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1330–1335, Hong Kong, China. Association for Computational Linguistics.