融合特定语言适配模块的多语言神经机器翻译  

Incorporating Language-specific Adapter into Multilingual Neural Machine Translation

作　　者：刘俊鹏 苏劲松 黄德根[1] LIU Jun-peng;SU Jin-song;HUANG De-gen(School of Computer Science and Technology,Dalian University of Technology,Dalian,Liaoning 116024,China;School of Informatics,Xiamen University,Xiamen,Fujian 361005,China)

机构地区：[1]大连理工大学计算机科学与技术学院,辽宁大连116024 [2]厦门大学信息学院,福建厦门361005

出　　处：《计算机科学》2022年第1期17-23,共7页Computer Science

基　　金：国家重点研发计划(2020AAA0108004)。

摘　　要：多语言神经机器翻译利用单一的编码器-解码器模型对多种语言之间的翻译同时进行建模。多语言神经机器翻译不仅能够促进关联语言之间的知识迁移,提高低资源语言的翻译质量,并且能够实现未见语言对之间的翻译。现有多语言神经机器翻译仍然存在语言多样性建模能力不足和未见语言对翻译质量不佳的问题。为此,首先在现有的适配器模型基础上提出变维双语适配器模型,在Transformer模型的每个子层之间加入双语适配器以抽取每个语言对的独特特征,并通过改变适配器隐层维度调整编码器和解码器两端的特定语言表达空间;其次,提出一种共享单语适配器模型,对每种语言的独特特征进行建模。在IWSLT多语言翻译数据集上的实验结果表明,变维双语适配器模型能够显著提升多语言翻译的性能,而单语适配器模型能够在不影响多语言翻译性能的条件下提高未见语言对的翻译质量。Multilingual neural machine translation(mNMT)leverages a single encoder-decoder model for translations in multiple language pairs.mNMT can encourage knowledge transfer among related languages,improve low-resource translation and enable zero-shot translation.However,the existing mNMT models are weak in modeling language diversity and perform poor zero-shot translation.To solve the above problems,we first propose a variable dimension bilingual adapter based on the existing adapter architecture.The bilingual adapters are introduced in-between each two Transformer sub-layers to extract language-pair-specific features and the language-pair-specific capacity in the encoder or the decoder can be altered by changing the inner dimension of adapters.We then propose a shared monolingual adapter to model unique features for each language.Experiments on IWSLT dataset show that the proposed model remarkably outperforms the multilingual baseline model and the monolingual adapter can improve the zero-shot translation without deteriorating the performance of multilingual translation.

关 键 词：多语言神经机器翻译 特定语言建模 双语适配器 单语适配器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]