Search Engine Guided Neural Machine Translation

Neural machine translation is a recently proposed paradigm A major technical challenge, other than designing such a in machine translation, where a single neural network, often neural machine translation system, is the scale of a training consisting of encoder and decoder recurrent networks, parallel corpus which often consists of hundreds of thousands is trained end-to-end to map from a source sentence to its to millions of sentence pairs. We address this issue by incorporating corresponding translation(Bahdanau, Cho, and Bengio 2014; an off-the-shelf black-box search engine into the Cho et al. 2014; Sutskever, Vinyals, and Le 2014; Kalchbrenner proposed neural machine translation system. The proposed and Blunsom 2013). The success of neural machine approach first queries a search engine, which indexes a whole translation, which has already been adopted by major training set, with a given source sentence, and the proposed industry players in machine translation(Wu et al. 2016; neural translation system translates the source sentence while Crego et al. 2016), is often attributed to the advances in building incorporating all the retrieved training sentence pairs. In this and training recurrent networks as well as the availability way, the proposed translation system automatically adapts to of large-scale parallel corpora for machine translation.