摘要
随着深度学习技术的广泛应用,计算机对语言复杂性和上下文信息的理解能力显著提升。但术语翻译依然是机器翻译领域的难题,机器翻译译后编辑和自动译后编辑重点修改内容均与术语翻译相关。术语机器翻译主要面临数据资源缺乏和术语复杂性两大挑战。本研究认为,利用生成式人工智能的交互性,通过人机交互的术语译前编辑和人机交互的术语译后编辑,能够有效应对因术语复杂性和术语数据资源缺乏给机器翻译带来的挑战。翻译实践证明,这些方法能显著提升术语机器翻译的表现。
With the widespread adoption of deep learning technologies,computers have made significant progress in understanding linguistic complexity and contextual information.However,terminology translation remains a challenging issue in the field of machine translation and continues to be the primary focus of both machine translation postediting and automated post-editing.Terminology machine translation faces two main challenges:the scarcity of data resources and the complexity of terminologies.This study argues that by leveraging the interactive capabilities of generative artificial intelligence,particularly through humancomputer interactive pre-editing and post-editing of terminologies,these challenges can be effectively addressed.Our translation practices have proven that these methods can substantially improve the performance of terminology machine translation.
作者
郑国锋
潘曦
ZHENG Guofeng;PAN Xi
出处
《中国外语》
北大核心
2025年第4期96-104,共9页
Foreign Languages in China
关键词
术语机器翻译
人机交互
术语译前编辑
术语译后编辑
terminology machine translation
humancomputer interaction
terminology pre-editing
terminology post-editing
