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    <titleInfo>
        <title>Large-scale Word Alignment Using Soft Dependency Cohesion Constraints</title>
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    <name type="personal">
        <namePart type="given">Zhiguo</namePart>
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    <name type="personal">
        <namePart type="given">Chengqing</namePart>
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    <abstract>Dependency cohesion refers to the observation that phrases dominated by disjoint dependency subtrees in the source language generally do not overlap in the target language. It has been verified to be a useful constraint for word alignment. However, previous work either treats this as a hard constraint or uses it as a feature in discriminative models, which is ineffective for large-scale tasks. In this paper, we take dependency cohesion as a soft constraint, and integrate it into a generative model for large-scale word alignment experiments. We also propose an approximate EM algorithm and a Gibbs sampling algorithm to estimate model parameters in an unsupervised manner. Experiments on large-scale Chinese-English translation tasks demonstrate that our model achieves improvements in both alignment quality and translation quality.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00228</identifier>
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        <date>2013</date>
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            <start>291</start>
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%0 Journal Article
%T Large-scale Word Alignment Using Soft Dependency Cohesion Constraints
%A Wang, Zhiguo
%A Zong, Chengqing
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F wang-zong-2013-large
%X Dependency cohesion refers to the observation that phrases dominated by disjoint dependency subtrees in the source language generally do not overlap in the target language. It has been verified to be a useful constraint for word alignment. However, previous work either treats this as a hard constraint or uses it as a feature in discriminative models, which is ineffective for large-scale tasks. In this paper, we take dependency cohesion as a soft constraint, and integrate it into a generative model for large-scale word alignment experiments. We also propose an approximate EM algorithm and a Gibbs sampling algorithm to estimate model parameters in an unsupervised manner. Experiments on large-scale Chinese-English translation tasks demonstrate that our model achieves improvements in both alignment quality and translation quality.
%R 10.1162/tacl_a_00228
%U https://aclanthology.org/Q13-1024
%U https://doi.org/10.1162/tacl_a_00228
%P 291-300

Markdown (Informal)

[Large-scale Word Alignment Using Soft Dependency Cohesion Constraints](https://aclanthology.org/Q13-1024) (Wang & Zong, TACL 2013)

• Large-scale Word Alignment Using Soft Dependency Cohesion Constraints (Wang & Zong, TACL 2013)

ACL

• Zhiguo Wang and Chengqing Zong. 2013. Large-scale Word Alignment Using Soft Dependency Cohesion Constraints. Transactions of the Association for Computational Linguistics, 1:291–300.