In this paper, we propose a novel recursive recurrent neural network (R 2 NN) to model the end-to-end decoding process for statistical machine translation. R 2 NN 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 neural networks, so that language model and translation model can be integrated naturally; (2) a tree structure can be built, as recursive neural networks, so as to generate the translation candidates in a bottom up manner. A semi-supervised training approach is proposed to train the parameters, and the phrase pair embedding is explored to model translation confidence directly. Experiments on a Chinese to English translation task show that our proposed R 2 NN can outperform the stateof-the-art baseline by about 1.5 points in BLEU.
A Recursive Recurrent Neural Network for Statistical Machine Translation
Shujie Liu,Nan Yang,Mu Li,M. Zhou
Published 2014 in Annual Meeting of the Association for Computational Linguistics
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- Publication year
2014
- Venue
Annual Meeting of the Association for Computational Linguistics
- Publication date
2014-06-01
- Fields of study
Computer Science
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