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        <title>Connecting Phrase based Statistical Machine Translation Adaptation</title>
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    <name type="personal">
        <namePart type="given">Rui</namePart>
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        <namePart type="given">Hai</namePart>
        <namePart type="family">Zhao</namePart>
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    <name type="personal">
        <namePart type="given">Bao-Liang</namePart>
        <namePart type="family">Lu</namePart>
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    <name type="personal">
        <namePart type="given">Masao</namePart>
        <namePart type="family">Utiyama</namePart>
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    <name type="personal">
        <namePart type="given">Eiichiro</namePart>
        <namePart type="family">Sumita</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <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>Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains of the original corpus can indeed enhance SMT performance directly. A series of SMT adaptation methods have been proposed to select these similar-domain data, and most of them focus on sentence selection. In comparison, phrase is a smaller and more fine grained unit for data selection, therefore we propose a straightforward and efficient connecting phrase based adaptation method, which is applied to both bilingual phrase pair and monolingual n-gram adaptation. The proposed method is evaluated on IWSLT/NIST data sets, and the results show that phrase based SMT performances are significantly improved (up to +1.6 in comparison with phrase based SMT baseline system and +0.9 in comparison with existing methods).</abstract>
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        <url>https://aclanthology.org/C16-1295</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T Connecting Phrase based Statistical Machine Translation Adaptation
%A Wang, Rui
%A Zhao, Hai
%A Lu, Bao-Liang
%A Utiyama, Masao
%A Sumita, Eiichiro
%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 wang-etal-2016-connecting
%X Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains of the original corpus can indeed enhance SMT performance directly. A series of SMT adaptation methods have been proposed to select these similar-domain data, and most of them focus on sentence selection. In comparison, phrase is a smaller and more fine grained unit for data selection, therefore we propose a straightforward and efficient connecting phrase based adaptation method, which is applied to both bilingual phrase pair and monolingual n-gram adaptation. The proposed method is evaluated on IWSLT/NIST data sets, and the results show that phrase based SMT performances are significantly improved (up to +1.6 in comparison with phrase based SMT baseline system and +0.9 in comparison with existing methods).
%U https://aclanthology.org/C16-1295
%P 3135-3145

Markdown (Informal)

[Connecting Phrase based Statistical Machine Translation Adaptation](https://aclanthology.org/C16-1295) (Wang et al., COLING 2016)

• Connecting Phrase based Statistical Machine Translation Adaptation (Wang et al., COLING 2016)

ACL

• Rui Wang, Hai Zhao, Bao-Liang Lu, Masao Utiyama, and Eiichiro Sumita. 2016. Connecting Phrase based Statistical Machine Translation Adaptation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3135–3145, Osaka, Japan. The COLING 2016 Organizing Committee.