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        <title>Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation</title>
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        <namePart type="given">Laura</namePart>
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            <title>Proceedings of the Third Workshop on Discourse in Machine Translation</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Andrei</namePart>
            <namePart type="family">Popescu-Belis</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jörg</namePart>
            <namePart type="family">Tiedemann</namePart>
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    <abstract>Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88% of the changes, improving the translation in 36%-48% of them over the baseline.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4813</identifier>
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%0 Conference Proceedings
%T Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation
%A Mascarell, Laura
%Y Webber, Bonnie
%Y Popescu-Belis, Andrei
%Y Tiedemann, Jörg
%S Proceedings of the Third Workshop on Discourse in Machine Translation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F mascarell-2017-lexical
%X Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88% of the changes, improving the translation in 36%-48% of them over the baseline.
%R 10.18653/v1/W17-4813
%U https://aclanthology.org/W17-4813
%U https://doi.org/10.18653/v1/W17-4813
%P 99-109

Markdown (Informal)

[Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation](https://aclanthology.org/W17-4813) (Mascarell, DiscoMT 2017)

• Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation (Mascarell, DiscoMT 2017)

ACL

• Laura Mascarell. 2017. Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation. In Proceedings of the Third Workshop on Discourse in Machine Translation, pages 99–109, Copenhagen, Denmark. Association for Computational Linguistics.