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        <title>Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues</title>
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        <namePart type="given">Xiao</namePart>
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        <namePart type="given">Laura</namePart>
        <namePart type="family">Mascarell</namePart>
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    <name type="personal">
        <namePart type="given">Andrei</namePart>
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            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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            <namePart type="given">Phil</namePart>
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    <abstract>We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently, i.e. using the same noun in the target text as well. We train and test classifiers that predict consistent translations based on lexical, syntactic, and semantic features. We first evaluate the accuracy of our classifiers intrinsically, in terms of the accuracy of consistency predictions, over a subset of the UN Corpus. Then, we also evaluate them in combination with phrase-based statistical MT systems for Chinese-to-English and German-to-English. We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers’ output, and also use these methods in combination. This improves over the baseline and closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier.</abstract>
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%0 Conference Proceedings
%T Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues
%A Pu, Xiao
%A Mascarell, Laura
%A Popescu-Belis, Andrei
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F pu-etal-2017-consistent
%X We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently, i.e. using the same noun in the target text as well. We train and test classifiers that predict consistent translations based on lexical, syntactic, and semantic features. We first evaluate the accuracy of our classifiers intrinsically, in terms of the accuracy of consistency predictions, over a subset of the UN Corpus. Then, we also evaluate them in combination with phrase-based statistical MT systems for Chinese-to-English and German-to-English. We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers’ output, and also use these methods in combination. This improves over the baseline and closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier.
%U https://aclanthology.org/E17-1089
%P 948-957

Markdown (Informal)

[Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues](https://aclanthology.org/E17-1089) (Pu et al., EACL 2017)

• Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues (Pu et al., EACL 2017)

ACL

• Xiao Pu, Laura Mascarell, and Andrei Popescu-Belis. 2017. Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 948–957, Valencia, Spain. Association for Computational Linguistics.