Neural Post-Editing Based on Quality Estimation

Proceedings of the Conference on Machine Translation (WMT), Volume 2: Shared Task Papers, pages 655–660 Copenhagen, Denmark, September 711, 2017. c⃝2017 Association for Computational Linguistics 1 Neural Post-Editing Based on Quality Estimation Yiming Tan, Zhiming Chen, Liu Huang, Lilin Zhang, Maoxi Li, Mingwen Wang School of Computer Information Engineering, Jiangxi Normal University {tt_yymm, qqchenzhiming, ufhuangliu, lilinzhang, mosesli, mwwang} @jxnu.edu.cn Abstract Automatic post-editing (APE) is a challenging task on WMT evaluation campaign. We find that only a small number of edit operations are required for most machine translation outputs, through analysis of the training set of WMT17 APE en-de task. Based on this statistics analysis, two neural post- editing (NPE) models are trained depended on the edit numbers: single edit and minor edits. The improved quality estimation (QE) approach is exploited to rank models, and select the best translation as the post-edited output from the n-best list translation hypotheses generated by the best APE model and the raw translation system. Experimental results on the datasets of WMT16 APE test set show that the proposed approach significantly outp- erformed the baseline. Our approach can bring considerable relief from the overcorrection problem in APE. 1 Introduction Automatic post-editing (APE) aims to learn how to correct machine translation errors by use of the human post-editing feedback. The traditional statistical post-editing builds monolingual statistical phrased-based machine translation system to translate the wrong raw outputs into good translations (Simard et al., 2007; Bechara et al., 2011; Chatterjee et al., 2015). In recent years, with the great success of deep learning achieved in machine translation, many works have applied neural machine translation (NMT) to the APE task. Pal et al. proposed to exploit the bidirectional source RNN encoder-decoder model to establish a monolingual machine translation system for APE (Pal et al., 2016). Compared with the tradi- tional statistical post-editing approaches, their approach gained more improvement. In the light of the context information of the translation, Pecina et al. proposed to respectively establish independent encoders for source sentences and raw machine translations (Pecina et al., 2016). Their approach is similar to the multi-source NMT (Zoph et al., 2016); the difference lies in the input information are source sentences and raw machine translation outputs. Grundkiewicz et al. proposed to combine the outputs of monolin- gual NMT and bilingual NMT to improve the performance of APE task (Grundkiewicz et al., 2016). This paper presents a new approach for APE which was submitted by the JXNU team to WMT17 APE shared task. In order to effectively reduce the overcorrection problem, we propose to build two specific neural post-editing (NPE) models in term of the edit numbers, and select the best model by machine translation quality estima- tion (QE). The experiment results indicate that the proposed approach gains great improvement over the baseline officially released by the eval- uation campaign. 2 Data analysis Overcorrection problem refers to edit the machine translation output more times than it really needed, among these edit operations, some are not necessary or even wrong. Overcorrection may cause …

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