Paper: Additive Neural Networks for Statistical Machine Translation

ACL ID P13-1078
Title Additive Neural Networks for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 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 mod...