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Channel-Aware Decoupling Network for Multiturn Dialog Comprehension

文献类型：期刊论文

作者Zhang, Zhuosheng3,4; Zhao, Hai3,4; Liu, Longxiang1,2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-11-14
页码12
关键词Deep neural networks dialog modeling natural language generation open domain conversation system
ISSN号2162-237X
DOI10.1109/TNNLS.2022.3220047
英文摘要Training machines to understand natural language and interact with humans is one of the major goals of artificial intelligence. Recent years have witnessed an evolution from matching networks to pretrained language models (PrLMs). In contrast to the plain-text modeling as the focus of the PrLMs, dialog texts involve multiple speakers and reflect special characteristics, such as topic transitions and structure dependencies, between distant utterances. However, the related PrLM models commonly represent dialogs sequentially by processing the pairwise dialog history as a whole. Thus, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose compositional learning for holistic interaction across the utterances beyond the sequential contextualization from PrLMs, in order to capture the utterance-aware and speaker-aware representations entailed in a dialog history. We decouple the contextualized word representations by masking mechanisms in transformer-based PrLM, making each word only focus on the words in the current utterance, other utterances, and two speaker roles (i.e., utterances of the sender and utterances of the receiver), respectively. In addition, we employ domain-adaptive training strategies to help the model adapt to the dialog domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets, achieving new state-of-the-art performance over previous methods.
资助项目Key Projects of National Natural Science Foundation of China[U1836222] ; Key Projects of National Natural Science Foundation of China[61733011]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000886836300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/19872]  
专题中国科学院计算技术研究所期刊论文
通讯作者Zhao, Hai
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100045, Peoples R China
2.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China
3.Shanghai Jiao Tong Univ, MoE Key Lab Artificial Intelligence, Shanghai 200240, Peoples R China
4.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Key Lab Shanghai Educ Commiss Intelligent Interact, Shanghai 200240, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Zhuosheng,Zhao, Hai,Liu, Longxiang. Channel-Aware Decoupling Network for Multiturn Dialog Comprehension[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:12.
APA Zhang, Zhuosheng,Zhao, Hai,&Liu, Longxiang.(2022).Channel-Aware Decoupling Network for Multiturn Dialog Comprehension.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA Zhang, Zhuosheng,et al."Channel-Aware Decoupling Network for Multiturn Dialog Comprehension".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):12.

入库方式： OAI收割

来源：计算技术研究所

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