b). Lexical Chain Based Cohesion Models for Document-Level Statistical Machine Translation

Lexical chains provide a representation of the lexical cohesion structure of a text. In this pa-per, we propose two lexical chain based co-hesion models to incorporate lexical cohesion into document-level statistical machine trans-lation: 1) a count cohesion model that rewards a hypothesis whenever a chain word occurs in the hypothesis, 2) and a probability cohesion model that further takes chain word transla-tion probabilities into account. We compute lexical chains for each source document to be translated and generate target lexical chains based on the computed source chains via max-imum entropy classifiers. We then use the generated target chains to provide constraints for word selection in document-level machine translation through the two proposed lexical chain based cohesion models. We verify the effects of the two models using a hierarchical phrase-based translation system. Experiments on large-scale training data show that they can substantially improve translation quality in terms of BLEU and that the probability cohesion model outperforms previous models based on lexical cohesion devices.