Word Alignment Modeling with Context Dependent Deep Neural Network
Nan Yang, Shujie Liu, Mu Li, Ming Zhou and Nenghai Yu
The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013
In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNN HMM(Dahl et al., 2012) method introduced in speech recognition to the HMM-based word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale English-Chinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.
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