A Recursive Recurrent Neural Network for Statistical Machine Translation

In this paper, we propose a novel recursive recurrent neural network (R2NN) to mod-el the end-to-end decoding process for s-tatistical machine translation. R2NN is a combination of recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to generate the next hidden state, like recurrent neu-ral networks, so that language model and translation model can be integrated natu-rally; (2) a tree structure can be built, as recursive neural networks, so as to gener-ate the translation candidates in a bottom up manner. A semi-supervised training ap-proach is proposed to train the parameter-s, and the phrase pair embedding is ex-plored to model translation confidence di-rectly. Experiments on a Chinese to En-glish translation task show that our pro-posed R2NN can outperform the state-of-the-art baseline by about 1.5 points in BLEU