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Robust Cross-lingual Task-oriented Dialogue

文献类型：期刊论文

作者Xiang, Lu1,2; Zhu, Junnan1,2; Zhao, Yang1,2; Zhou, Yu1,2; Zong, Chengqing1,2
刊名ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
出版日期2021-11-01
卷号20期号:6页码:24
ISSN号2375-4699
关键词Cross-lingual dialogue system adversarial learning knowledge robustness
DOI10.1145/3457571
通讯作者Xiang, Lu(lu.xiang@nlpr.ia.ac.cn)
英文摘要Cross-lingual dialogue systems are increasingly important in e-commerce and customer service due to the rapid progress of globalization. In real-world system deployment, machine translation (MT) services are often used before and after the dialogue system to bridge different languages. However, noises and errors introduced in the MT process will result in the dialogue system's low robustness, making the system's performance far from satisfactory. In this article, we propose a novel MT-oriented noise enhanced framework that exploits multi-granularityMTnoises and injects such noises into the dialogue system to improve the dialogue system's robustness. Specifically, we first design a method to automatically construct multi-granularity MT-oriented noises and multi-granularity adversarial examples, which contain abundant noise knowledge oriented to MT. Then, we propose two strategies to incorporate the noise knowledge: (i) Utterance-level adversarial learning and (ii) Knowledge-level guided method. The former adopts adversarial learning to learn a perturbation-invariant encoder, guiding the dialogue system to learn noise-independent hidden representations. The latter explicitly incorporates the multi-granularity noises, which contain the noise tokens and their possible correct forms, into the training and inference process, thus improving the dialogue system's robustness. Experimental results on three dialoguemodels, two dialogue datasets, and two language pairs have shown that the proposed framework significantly improves the performance of the cross-lingual dialogue system.
WOS关键词SPOKEN ; NETWORKS
资助项目National Key Research and Development Program of China[2017YFB1002103]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:000721586800002
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/46443]  
专题模式识别国家重点实验室_自然语言处理
通讯作者Xiang, Lu
作者单位1.Intelligence Bldg,95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Sch Artificial Intelligence,Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xiang, Lu,Zhu, Junnan,Zhao, Yang,et al. Robust Cross-lingual Task-oriented Dialogue[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2021,20(6):24.
APA Xiang, Lu,Zhu, Junnan,Zhao, Yang,Zhou, Yu,&Zong, Chengqing.(2021).Robust Cross-lingual Task-oriented Dialogue.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,20(6),24.
MLA Xiang, Lu,et al."Robust Cross-lingual Task-oriented Dialogue".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 20.6(2021):24.

入库方式： OAI收割

来源：自动化研究所

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