基于用户行为模型的计算机辅助翻译方法

Computer Assisted Translation Based on User Behavior Model

与全自动机器翻译相比,计算机辅助翻译技术更具实用性,已成为机器翻译领域的一个研究热点。传统的辅助翻译过程中,用户只能被动接受系统提供的辅助译文,并进行翻译后编辑操作。该文提出一种基于用户行为模型的辅助翻译方法,通过实时记录用户的后编辑过程,分析出用户的翻译决策,建立用户行为模型,使得翻译系统能够动态获取和共享用户的翻译知识,从而提高辅助译文的质量。实验结果表明,在同一篇文档前30%文本的后编辑过程中建立的用户行为模型,使余下70%文本的辅助译文的BLEU值平均提高了4.9%,用户模型中翻译知识的准确率达到94.1%。

Compared with the automatic machine translation,the computer assisted translation is more practical for real applications.In traditional computer assisted translation,users can only passively accept the translation provided by the system and perform post-editing on it.This paper proposes a computer assisted translation approach based on user behavior model,in which users＇ explicit behaviors in the post-editing process are recorded and users＇ translation decisions are discovered.In this way,the system can dynamically acquire and share users＇ translation knowledge to improve the quality of aided translation.Experimental results show that the user behavior model built on the post-editing of the first 30% text in a document improves the BLEU score of the translation candidates for the remaining 70% text by 4.9%.The precision of the translation knowledge in user model achieves 94.1%.