Paper: Word Alignment Modeling with Context Dependent Deep Neural Network

ACL ID P13-1017
Title Word Alignment Modeling with Context Dependent Deep Neural Network
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

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 ...