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Topic-driven and knowledge-aware transformer for dialogue emotion detection

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Zhu, Lixing, Pergola, Gabriele, Gui, Lin, Zhou, Deyu and He, Yulan (2021) Topic-driven and knowledge-aware transformer for dialogue emotion detection. In: ACL 2021, Online, 2–4 Aug 2021. Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language pp. 1571-1582.

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Abstract

Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.

Item Type: Conference Item (Paper)
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Information retrieval, Knowledge representation (Information theory), Emotion recognition , Natural language processing (Computer science) , Computational linguistics, Sentiment analysis
Journal or Publication Title: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language
Publisher: Association for Computational Linguistics
Official Date: 2021
Dates:
DateEvent
2021Available
5 May 2021Accepted
Page Range: pp. 1571-1582
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: ©2021 Association for Computational Linguistics
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/T017112/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/V048597/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
Chancellor’s International ScholarshipUniversity of Warwickhttp://dx.doi.org/10.13039/501100000741
EP/V020579/1UK Research and Innovationhttp://dx.doi.org/10.13039/100014013
2017YFB1002801National Key Research and Development Program of ChinaUNSPECIFIED
61772132[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
Conference Paper Type: Paper
Title of Event: ACL 2021
Type of Event: Conference
Location of Event: Online
Date(s) of Event: 2–4 Aug 2021
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