Semi Supervised Learning for Neural Machine Translation
While end to end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source to target and target to source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese English dataset show that our approach achieves significant improvements over state of the art SMT and NMT systems.
ACL