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        <title>Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix</title>
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    <name type="personal">
        <namePart type="given">Na</namePart>
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        <namePart type="given">Guiping</namePart>
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    <name type="personal">
        <namePart type="given">Dongfeng</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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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. Focusing on this problem, this paper mines the deep constraints of prefix on syntactic level to improve the performance of IPMT systems. Two syntactic subtree matching rules based on phrase structure grammar are proposed to filter the translation hypotheses more strictly. Experimental results on LDC Chinese-English corpora show that the proposed method outperforms state-of-the-art phrase-based IPMT system while keeping comparable decoding speed.</abstract>
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        <url>https://aclanthology.org/C16-1169/</url>
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%0 Conference Proceedings
%T Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix
%A Ye, Na
%A Zhang, Guiping
%A Cai, Dongfeng
%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 ye-etal-2016-interactive
%X Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. Focusing on this problem, this paper mines the deep constraints of prefix on syntactic level to improve the performance of IPMT systems. Two syntactic subtree matching rules based on phrase structure grammar are proposed to filter the translation hypotheses more strictly. Experimental results on LDC Chinese-English corpora show that the proposed method outperforms state-of-the-art phrase-based IPMT system while keeping comparable decoding speed.
%U https://aclanthology.org/C16-1169/
%P 1797-1806

Markdown (Informal)

[Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix](https://aclanthology.org/C16-1169/) (Ye et al., COLING 2016)

• Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix (Ye et al., COLING 2016)

ACL

• Na Ye, Guiping Zhang, and Dongfeng Cai. 2016. Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1797–1806, Osaka, Japan. The COLING 2016 Organizing Committee.