融合目标端上下文的篇章神经机器翻译
Modeling Target-side Context for Document-level Neural Machine Translation
贾爱鑫 1李军辉 1贡正仙 1张民1
作者信息
- 1. 苏州大学 计算机科学与技术学院,江苏 苏州 215006
- 折叠
摘要
神经机器翻译在句子级翻译任务上取得了令人瞩目的效果,但是句子级翻译的译文会存在一致性、指代等篇章问题,篇章翻译通过利用上下文信息来解决上述问题.不同于以往使用源端上下文建模的方法,该文提出了融合目标端上下文信息的篇章神经机器翻译.具体地,该文借助推敲网络的思想,对篇章源端进行二次翻译,第一次基于句子级翻译,第二次翻译参考了全篇的第一次翻译结果.基于 LDC 中英篇章数据集和 WMT 英德篇章数据集的实验结果表明,在引入较少的参数的条件下,该文方法能显著提高翻译性能.同时,随着第一次翻译(即句子级译文)质量的提升,所提方法也更有效.
Abstract
Recently neural machine translation(NMT)has achieved remarkable success in sentence-level translation.However,it still cannot resolve a wide variety of discourse phenomena,such as lexical cohesion and coreference,which can be alleviated by using context information in document-level translation.In contrast to existing studies of modeling source-side context,this paper proposes to model target-side context in document-level NMT.Specifically,motivated by deliberation networks,our approach translates source-side document twice.In the first-pass transla-tion,it performs sentence-level translation.In the second-pass,it properly translates each sentence by modeling the target-side context which has just be generated from the first-pass translation.Experimental results on LDC Chinese-to-English and WMT English-to-German document-level translation tasks show that our approach significantly im-proves translation performance by introducing few parameters.Meanwhile,it is observed that the proposed approach benefits more if the performance of the first-pass translation(i.e.,sentence-level NMT)is improved.
关键词
神经机器翻译/推敲网络/篇章翻译Key words
neural machine translation/deliberation networks/document-level translation引用本文复制引用
基金项目
国家自然科学基金(61876120)
国家自然科学基金(61976148)
出版年
2024
NETL
NSTL
万方数据