Bridging the Gap between Training and Inference for Neural Machine Translation

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4334–4343 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 4334 Bridging the Gap between Training and Inference for Neural Machine Translation Wen Zhang1,2 Yang Feng1,2∗ Fandong Meng3 Di You4 Qun Liu5 1Key Laboratory of Intelligent Information Processing Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) 2University of Chinese Academy of Sciences, Beijing, China {zhangwen,fengyang}@ict.ac.cn 3Pattern Recognition Center, WeChat AI, Tencent Inc, China fandongmeng@tencent.com 4Worcester Polytechnic Institute, Worcester, MA, USA dyou@wpi.edu 5Huawei Noah’s Ark Lab, Hong Kong, China qun.liu@huawei.com Abstract Neural Machine Translation (NMT) generates target words sequentially in the way of pre- dicting the next word conditioned on the con- text words. At training time, it predicts with the ground truth words as context while at in- ference it has to generate the entire sequence from scratch. This discrepancy of the fed con- text leads to error accumulation among the way. Furthermore, word-level training re- quires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but rea- sonable translations. In this paper, we ad- dress these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence by the model during training, where the predicted sequence is selected with a sentence-level optimum. Experiment results on Chinese→English and WMT’14 English→German translation tasks demonstrate that our approach can achieve sig- nificant improvements on multiple datasets. 1 Introduction Neural Machine Translation has shown promising results and drawn more attention recently. Most NMT models fit in the encoder-decoder frame- work, including the RNN-based (Sutskever et al., 2014; Bahdanau et al., 2015; Meng and Zhang, 2019), the CNN-based (Gehring et al., 2017) and the attention-based (Vaswani et al., 2017) mod- els, which predict the next word conditioned on the previous context words, deriving a language model over target words. The scenario is at train- ing time the ground truth words are used as context ∗Corresponding author. while at inference the entire sequence is generated by the resulting model on its own and hence the previous words generated by the model are fed as context. As a result, the predicted words at train- ing and inference are drawn from different dis- tributions, namely, from the data distribution as opposed to the model distribution. This discrep- ancy, called exposure bias (Ranzato et al., 2015), leads to a gap between training and inference. As the target sequence grows, the errors accumulate among the sequence and the model has to predict under the condition it has never met at training time. Intuitively, to address this problem, the model should be trained to predict under the same con- dition it will face at inference. Inspired by DATA AS DEMONSTRATOR (DAD) (Venkatraman et al., 2015), feeding as context both ground truth words and the predicted words during training can be a solution. NMT models usually optimize the cross-entropy loss which requires a strict pairwise matching at the …

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