神经机器翻译中的语言歧义消解问题及解决方法  

Resolving Language Ambiguity in Neural Machine Translation:Problems and Solutions

作　　者：向昕舸 Xinge Xiang(Wuhan University of Communication,Wuhan,Hubei,30205,China)

机构地区：[1]武汉传媒学院,湖北武汉430205

出　　处：《教育研究前沿（中英文版）》2024年第3期93-99,共7页Education Research Frontier

摘　　要：随着全球化的加速,跨语言交流的需求不断增长,神经机器翻译(NMT)作为应对此需求的关键技术,已广泛应用于多种语言服务。然而,语言的固有复杂性,特别是语言歧义问题,对翻译的准确性和自然度构成了严峻挑战。本文系统探讨了NMT中的语言歧义问题,包括词汇歧义、句法歧义和语义歧义,并详细分析了造成这些问题的因素。进一步,本文提出了多种解决语言歧义的策略,如利用语境信息、多任务学习与知识迁移等,旨在提高翻译质量和系统的灵活性。此外,本文还探讨了未来研究的方向和面临的挑战,包括深层语义理解的增强、模型创新、数据驱动的挑战以及实际应用的推广。As globalization accelerates,the demand for cross-language communication continues to grow,making Neural Machine Translation(NMT)a critical technology widely applied in various language services.However,the inherent complexity of language,especially issues related to language ambiguity,poses serious challenges to the accuracy and naturalness of translations.This paper systematically explores language ambiguity issues in NMT,including lexical,syntactic,and semantic ambiguities,and analyzes the factors contributing to these problems.Furthermore,it proposes several strategies to resolve language ambiguities,such as utilizing contextual information,multi-task learning,and knowledge transfer,aimed at enhancing translation quality and system flexibility.Additionally,the paper discusses future research directions and challenges,including enhancing deep semantic understanding,model innovation,data-driven challenges,and the expansion of practical applications.

关 键 词：神经机器翻译 语言歧义 多任务学习 

分 类 号：H31[语言文字—英语]