Train the Machine with What It Can Learn - Corpus Selection for SMT

Statistical machine translation relies heavily on available parallel corpora, but SMT may not have the ability or intelligence to make full use of the training set. Instead of collecting more and more parallel training corpora, this paper aims to improve SMT performance by exploiting the full potential of existing parallel corpora. We first identify literally translated sentence pairs via lexical and grammatical compatibility, and then use these data to train SMT models. One experiment indicates that larger training corpora do not always lead to higher decoding performance when the added data are not literal translations. And another experiment shows that properly enlarging the contribution of literal translation can improve SMT performance significantly.