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›› 2017, Vol. 32 ›› Issue (4): 796-804.

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Issue on Deep Learning •

### Optimizing Non-Decomposable Evaluation Metrics for Neural Machine Translation

Shi-Qi Shen1,2,3, Student Member, CCF, Yang Liu1,2,3,4,*, Senior Member, CCF, Mao-Song Sun1,2,3,4, Senior Member, CCF

1. 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2 State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
4 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China
• Received:2016-12-20 Revised:2017-05-20 Online:2017-07-05 Published:2017-07-05
• Contact: Yang Liu E-mail:liuyang2011@tsinghua.edu.cn
• Supported by:

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61522204, 61432013, and the National High Technology Research and Development 863 Program of China under Grant No. 2015AA015407, also supported by the Singapore National Research Foundation under Its International Research Centre@Singapore Funding Initiative, and administered by the IDM (Interactive Digital Media) Programme.

While optimizing model parameters with respect to evaluation metrics has recently proven to benefit endto-end neural machine translation (NMT), the evaluation metrics used in the training are restricted to be defined at the sentence level to facilitate online learning algorithms. This is undesirable because the final evaluation metrics used in the testing phase are usually non-decomposable (i.e., they are defined at the corpus level and cannot be expressed as the sum of sentence-level metrics). To minimize the discrepancy between the training and the testing, we propose to extend the minimum risk training (MRT) algorithm to take non-decomposable corpus-level evaluation metrics into consideration while still keeping the advantages of online training. This can be done by calculating corpus-level evaluation metrics on a subset of training data at each step in online training. Experiments on Chinese-English and English-French translation show that our approach improves the correlation between training and testing and significantly outperforms the MRT algorithm using decomposable evaluation metrics.

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