@inproceedings{nadejde-etal-2016-neural,
title = "A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation",
author = "N{\u{a}}dejde, Maria and
Birch, Alexandra and
Koehn, Philipp",
editor = {Cettolo, Mauro and
Niehues, Jan and
St{\"u}ker, Sebastian and
Bentivogli, Luisa and
Cattoni, Rolando and
Federico, Marcello},
booktitle = "Proceedings of the 13th International Conference on Spoken Language Translation",
month = dec # " 8-9",
year = "2016",
address = "Seattle, Washington D.C",
publisher = "International Workshop on Spoken Language Translation",
url = "https://aclanthology.org/2016.iwslt-1.11",
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{\%}.",
}
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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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<namePart type="given">Maria</namePart>
<namePart type="family">Nădejde</namePart>
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<title>Proceedings of the 13th International Conference on Spoken Language Translation</title>
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<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)
ACL