Minimum Risk Training for Neural Machine Translation

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1683–1692, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Minimum Risk Training for Neural Machine Translation Shiqi Shen†, Yong Cheng#, Zhongjun He+, Wei He+, Hua Wu+, Maosong Sun†, Yang Liu†∗ †State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, China #Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China +Baidu Inc., Beijing, China {vicapple22, chengyong3001}@gmail.com, {hezhongjun, hewei06, wu hua}@baidu.com, {sms, liuyang2011}@tsinghua.edu.cn Abstract We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is ca- pable of optimizing model parameters di- rectly with respect to arbitrary evaluation metrics, which are not necessarily differ- entiable. Experiments show that our ap- proach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs. Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks. 1 Introduction Recently, end-to-end neural machine transla- tion (NMT) (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Bahdanau et al., 2015) has attracted increasing attention from the com- munity. Providing a new paradigm for machine translation, NMT aims at training a single, large neural network that directly transforms a source- language sentence to a target-language sentence without explicitly modeling latent structures (e.g., word alignment, phrase segmentation, phrase re- ordering, and SCFG derivation) that are vital in conventional statistical machine translation (SMT) (Brown et al., 1993; Koehn et al., 2003; Chiang, 2005). Current NMT models are based on the encoder- decoder framework (Cho et al., 2014; Sutskever et al., 2014), with an encoder to read and encode a source-language sentence into a vector, from which a decoder generates a target-language sen- tence. While early efforts encode the input into a ∗Corresponding author: Yang Liu. fixed-length vector, Bahdanau et al. (2015) advo- cate the attention mechanism to dynamically gen- erate a context vector for a target word being gen- erated. Although NMT models have achieved results on par with or better than conventional SMT, they still suffer from a major drawback: the models are op- timized to maximize the likelihood of training data instead of evaluation metrics that actually quantify translation quality. Ranzato et al. (2015) indicate two drawbacks of maximum likelihood estimation (MLE) for NMT. First, the models are only ex- posed to the training distribution instead of model predictions. Second, the loss function is defined at the word level instead of the sentence level. In this work, we introduce minimum risk train- ing (MRT) for neural machine translation. The new training objective is to minimize the expected loss (i.e., risk) on the training data. MRT has the following advantages over MLE: 1. Direct optimization with respect to evalu- ation metrics: MRT introduces evaluation metrics as loss functions and aims to mini- mize expected loss on the training data. 2. Applicable to arbitrary loss functions: our approach …

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