Machine Translation Method Integrating New Energy Terminology Knowledge

• Published in: Ji suan ji ke xue Vol. 49; no. 6; pp. 305 - 312
• Main Authors: Dong, Zhen-Heng, Ren, Wei-Ping, You, Xin-Dong, Lyu, Xue-Qiang
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.06.2022; Editorial office of Computer Science
• Subjects: Alignment; Bilingualism; domain machine translation|domain terms|special identification|prior knowledge|term replacement|term append; Domains; English language; Knowledge; Knowledge bases (artificial intelligence); Machine translation; Splicing
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210500117

In domain machine translation,whether domain terms can be translated correctly plays a decisive role in translation quality.It is of practical significance to effectively integrate domain terms into neural machine translation model and improve the translation quality of domain terms.This paper proposes a method to integrate the term information in the field of new energy into neural machine translation as a priori knowledge.Taking the term dictionary constructed by the bilingual term knowledge base in the field of new energy as the medium,this paper puts forward and compares two different ways of knowledge integration:1)term replacement,that is,replacing the source term with the target term at the source language end;2)term addition refers to the splicing of source side terms and target side terms at the source language side,the identifier as special external knowledge is used to identify the beginning and end of the target term at both the source language end and the target language end.Experiments are carri