CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Large-scale Word Alignment Using Soft Dependency Cohesion Constraints
Zhiguo,Wang; Chengqing, Zong
Source PublicationTransactions of Association for Computational Linguistics(TACL)
AbstractDependency 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.
KeywordLarge-scale Word Alignment
Document Type期刊论文
Corresponding AuthorZhiguo,Wang
Recommended Citation
GB/T 7714
Zhiguo,Wang,Chengqing, Zong. Large-scale Word Alignment Using Soft Dependency Cohesion Constraints[J]. Transactions of Association for Computational Linguistics(TACL),2013(1):291-300.
APA Zhiguo,Wang,&Chengqing, Zong.(2013).Large-scale Word Alignment Using Soft Dependency Cohesion Constraints.Transactions of Association for Computational Linguistics(TACL)(1),291-300.
MLA Zhiguo,Wang,et al."Large-scale Word Alignment Using Soft Dependency Cohesion Constraints".Transactions of Association for Computational Linguistics(TACL) .1(2013):291-300.
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