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        <title>A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Nădejde</namePart>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Birch</namePart>
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    <name type="personal">
        <namePart type="given">Philipp</namePart>
        <namePart type="family">Koehn</namePart>
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            <title>Proceedings of the 13th International Conference on Spoken Language Translation</title>
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        <name type="personal">
            <namePart type="given">Mauro</namePart>
            <namePart type="family">Cettolo</namePart>
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        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Niehues</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Sebastian</namePart>
            <namePart type="family">Stüker</namePart>
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            <namePart type="given">Luisa</namePart>
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        <name type="personal">
            <namePart type="given">Rolando</namePart>
            <namePart type="family">Cattoni</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Marcello</namePart>
            <namePart type="family">Federico</namePart>
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    <abstract>String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model’s accuracy by 1.5% over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7%.</abstract>
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        <url>https://aclanthology.org/2016.iwslt-1.11</url>
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        <date>2016-dec 8-9</date>
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%0 Conference Proceedings
%T A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation
%A Nădejde, Maria
%A Birch, Alexandra
%A Koehn, Philipp
%Y Cettolo, Mauro
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Bentivogli, Luisa
%Y Cattoni, Rolando
%Y Federico, Marcello
%S Proceedings of the 13th International Conference on Spoken Language Translation
%D 2016
%8 dec 8 9
%I International Workshop on Spoken Language Translation
%C Seattle, Washington D.C
%F nadejde-etal-2016-neural
%X String-to-tree MT systems translate verbs without lexical or syntactic context on the source side and with limited target-side context. The lack of context is one reason why verb translation recall is as low as 45.5%. We propose a verb lexicon model trained with a feed-forward neural network that predicts the target verb conditioned on a wide source-side context. We show that a syntactic context extracted from the dependency parse of the source sentence improves the model’s accuracy by 1.5% over a baseline trained on a window context. When used as an extra feature for re-ranking the n-best list produced by the string-to-tree MT system, the verb lexicon model improves verb translation recall by more than 7%.
%U https://aclanthology.org/2016.iwslt-1.11

Markdown (Informal)

[A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation](https://aclanthology.org/2016.iwslt-1.11) (Nădejde et al., IWSLT 2016)

• A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation (Nădejde et al., IWSLT 2016)

ACL

• Maria Nădejde, Alexandra Birch, and Philipp Koehn. 2016. A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.