Enhancing Grammatical Cohesion: Generating Transitional Expressions for SMT

Transitional expressions provide glue that holds ideas together in a text and enhance the logical organization, which together help improve readability of a text. However, in most current statistical machine translation (SMT) systems, the outputs of compound-complex sentences still lack proper transitional expressions. As a result, the translations are often hard to read and understand. To address this issue, we propose two novel models to encourage generating such transitional expressions by introducing the source compoundcomplex sentence structure (CSS). Our models include a CSS-based translation model, which generates new CSS-based translation rules, and a generative transfer model, which encourages producing transitional expressions during decoding. The two models are integrated into a hierarchical phrase-based translation system to evaluate their effectiveness. The experimental results show that significant improvements are achieved on various test data meanwhile the translations are more cohesive and smooth.