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    <titleInfo>
        <title>Improving Translation Selection with Supersenses</title>
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    <name type="personal">
        <namePart type="given">Haiqing</namePart>
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        <namePart type="family">Xiong</namePart>
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    <name type="personal">
        <namePart type="given">Oier</namePart>
        <namePart type="family">Lopez de Lacalle</namePart>
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    <name type="personal">
        <namePart type="given">Eneko</namePart>
        <namePart type="family">Agirre</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good at detecting the proper sense for a polysemic word when it appears in different contexts. In this paper, we adopt a supersense tagging method to annotate source words with coarse-grained ontological concepts. In order to enable the system to choose an appropriate translation for a word or phrase according to the annotated supersense of the word or phrase, we propose two translation models with supersense knowledge: a maximum entropy based model and a supersense embedding model. The effectiveness of our proposed models is validated on a large-scale English-to-Spanish translation task. Results indicate that our method can significantly improve translation quality via correctly conveying the meaning of the source language to the target language.</abstract>
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%0 Conference Proceedings
%T Improving Translation Selection with Supersenses
%A Tang, Haiqing
%A Xiong, Deyi
%A Lopez de Lacalle, Oier
%A Agirre, Eneko
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F tang-etal-2016-improving-translation
%X Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good at detecting the proper sense for a polysemic word when it appears in different contexts. In this paper, we adopt a supersense tagging method to annotate source words with coarse-grained ontological concepts. In order to enable the system to choose an appropriate translation for a word or phrase according to the annotated supersense of the word or phrase, we propose two translation models with supersense knowledge: a maximum entropy based model and a supersense embedding model. The effectiveness of our proposed models is validated on a large-scale English-to-Spanish translation task. Results indicate that our method can significantly improve translation quality via correctly conveying the meaning of the source language to the target language.
%U https://aclanthology.org/C16-1293
%P 3114-3123

Markdown (Informal)

[Improving Translation Selection with Supersenses](https://aclanthology.org/C16-1293) (Tang et al., COLING 2016)

• Improving Translation Selection with Supersenses (Tang et al., COLING 2016)

ACL

• Haiqing Tang, Deyi Xiong, Oier Lopez de Lacalle, and Eneko Agirre. 2016. Improving Translation Selection with Supersenses. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3114–3123, Osaka, Japan. The COLING 2016 Organizing Committee.