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Zero-shot language extension for dialogue state tracking via pre-trained models and multi-auxiliary-tasks fine-tuning
Xiang, Lu1,2; Zhao, Yang1,2; Zhu, Junnan1,2; Zhou, Yu1,2,3; Zong, Chengqing1,2
Source PublicationKNOWLEDGE-BASED SYSTEMS
ISSN0950-7051
2023-01-10
Volume259Pages:14
Corresponding AuthorZhou, Yu(yzhou@nlpr.ia.ac.cn)
AbstractDialogue state tracking (DST), a crucial component of the task-oriented dialogue system (TOD), is designed to track the user's goal. Existing DST models mainly focus on monolingual dialogue input, failing to meet the growing needs of a TOD to provide multilingual services. Therefore, this paper proposes a novel Zero-shot Language Extension scenario for DST, extending the monolingual DST to multilingual DST without extra high-cost dialogue data annotation. In this scenario, the multilingual DST only needs a single shared model to handle multilingual input and generate a unified dialogue state. This setting makes deploying a complete multilingual TOD easy since it could be reused by the downstream components from existing monolingual TOD. Specifically, we achieve the language extension by multi-auxiliary-tasks fine-tuning of multilingual pre-trained models, where five relevant auxiliary tasks are jointly designed, including monolingual DST, cross-lingual DST, forward word translation, utterance recovery, and semantic similarity. The extended multilingual DST model can be enhanced through joint optimization with all the auxiliary tasks by capturing multilingual context understanding and cross-lingual alignment characteristics. Comprehensive experiments on Multilingual WOZ dataset (English -> German and English -> Italian) and cross-lingual MultiWOZ dataset (English -> Chinese and Chinese -> English) demonstrate the effectiveness and superiority of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.
KeywordDialogue state tracking Zero -shot language extension Multilingual DST Pre -trained models Multi -auxiliary -tasks fine-tuning
DOI10.1016/j.knosys.2022.110015
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China ; [2020AAA0108600]
Funding OrganizationNational Key R&D Program of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000883002100001
PublisherELSEVIER
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51249
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorZhou, Yu
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Zhongke Fanyu Technol Co Ltd, Fanyu AI Lab, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xiang, Lu,Zhao, Yang,Zhu, Junnan,et al. Zero-shot language extension for dialogue state tracking via pre-trained models and multi-auxiliary-tasks fine-tuning[J]. KNOWLEDGE-BASED SYSTEMS,2023,259:14.
APA Xiang, Lu,Zhao, Yang,Zhu, Junnan,Zhou, Yu,&Zong, Chengqing.(2023).Zero-shot language extension for dialogue state tracking via pre-trained models and multi-auxiliary-tasks fine-tuning.KNOWLEDGE-BASED SYSTEMS,259,14.
MLA Xiang, Lu,et al."Zero-shot language extension for dialogue state tracking via pre-trained models and multi-auxiliary-tasks fine-tuning".KNOWLEDGE-BASED SYSTEMS 259(2023):14.
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