Enriching SMT Training Data via Paraphrasing

This paper proposes a novel method to resolve the coverage problem of SMT system. The method generates paraphrases for source-side sentences of the bilingual parallel data, which are then paired with the target-side sentences to generate new parallel data. Within a statistical paraphrase generation framework, we employ an object function, named Sentence Novelty, to select paraphrases which having the most novel information to the bilingual training corpus of the SMT model. Meanwhile, the context is considered via a language model in the source language to ensure the fluency and accuracy of paraphrase substitution. Compared to a state-of-the-art phrase based SMT system (Moses), our method achieves an improvement of 1.66 points in terms of BLEU on a small training corpus which simulates a resource-poor environment, and 1.06 points on a training corpus of medium size.