Knowledge Commons of Institute of Automation,CAS
Robust Cross-lingual Task-oriented Dialogue | |
Xiang, Lu1,2; Zhu, Junnan1,2; Zhao, Yang1,2; Zhou, Yu1,2; Zong, Chengqing1,2 | |
发表期刊 | ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING |
ISSN | 2375-4699 |
2021-11-01 | |
卷号 | 20期号:6页码:24 |
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. |
关键词 | Cross-lingual dialogue system adversarial learning knowledge robustness |
DOI | 10.1145/3457571 |
关键词[WOS] | SPOKEN ; NETWORKS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFB1002103] |
项目资助者 | National Key Research and Development Program of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000721586800002 |
出版者 | ASSOC COMPUTING MACHINERY |
七大方向——子方向分类 | 自然语言处理 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46444 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
通讯作者 | Xiang, Lu |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Sch Artificial Intelligence,Univ Chinese Acad Sci, Beijing, Peoples R China 2.Intelligence Bldg,95 Zhongguancun East Rd, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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. |
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Robust.pdf(1935KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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