Word Alignment Modeling with Context Dependent Deep Neural Network
Nan Yang, Shujie Liu, Mu Li, Ming Zhou, Nenghai Yu · 2015
In this paper, we explore a novel bilin-gual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in var-ious 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 intro-duced in speech recognition to the HMM-based word alignment model, in which bilingual word embedding is discrimina-tively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being ca-pable to model the rich bilingual corre-spondence, our method generates a very compact model with much fewer parame-ters. 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. 1