Paper: Lexical Chain Based Cohesion Models for Document-Level Statistical Machine Translation

ACL ID D13-1163
Title Lexical Chain Based Cohesion Models for Document-Level Statistical Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

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 mo...