Additive Neural Networks for Statistical Machine Translation
Lemao Liu, Taro Watanabe, Eiichiro Sumita, Tiejun Zhao · 2013
Most statistical machine translation (SMT) systems are modeled using a log-linear framework. Although the log-linear model achieves success in SMT, it still suffers from some limitations: (1) the features are required to be linear with respect to the model itself; (2) features cannot be further interpreted to reach their potential. A neural network is a reasonable method to address these pitfalls. However, modeling SMT with a neural network is not trivial, especially when taking the decoding efficiency into consideration. In this paper, we propose a variant of a neural network, i.e. additive neural networks, for SMT to go beyond the log-linear translation model. In addition, word embedding is employed as the input to the neural network, which encodes each word as a feature vector. Our model outperforms the log-linear translation models with/without embed-ding features on Chinese-to-English and Japanese-to-English translation tasks.