Research on New Energy Patent Machine Translation Integrating Terminology Information
YOU Xindong1,YANG Haixiang1,CHEN Haitao2,SUN Tian1,2,LV Xueqiang1
1. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China;
2. School of Foreign Languages, Beijing Information Science and Technology University, Beijing 100192, China）
Abstract：The traditional neural machine translation is a black box and cannot effectively add terminology information. It is of practical significance to use term provided by the user to jointly train the neural machine translation model. Accordingly, we propose a new energy transformer patent machine translation model with terminology information incorporated. The source term is replaced with the target term and the target term is added after the source term to fusing the terminology information. Experimentsal results on the ChineseEnglish task with patent termbase in the field of new energy show that the proposed patent translation model is better than the Transformer baseline model, as well as the translation quality analysis on three datasets.
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