Improved Statistical Machine Translation Using Paraphrases

Parallel corpora are crucial for training SMT systems. However, for many lan-guage pairs they are available only in very limited quantities. For these lan-guage pairs a huge portion of phrases en-countered at run-time will be unknown. We show how techniques from paraphras-ing can be used to deal with these oth-erwise unknown source language phrases. Our results show that augmenting a state-of-the-art SMT system with paraphrases leads to significantly improved coverage and translation quality. For a training corpus with 10,000 sentence pairs we in-crease the coverage of unique test set un-igrams from 48 % to 90%, with more than half of the newly covered items accurately translated, as opposed to none in current approaches.