Multi-intent detection for task-oriented dialogue systems based on joint learning

Qian Zhang; Zesan Liu; Ziheng Wang; Xiaozhen Li

doi:10.1117/12.2661023

16 December 2022 Multi-intent detection for task-oriented dialogue systems based on joint learning

Qian Zhang, Zesan Liu, Ziheng Wang, Xiaozhen Li

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125005S (2022) https://doi.org/10.1117/12.2661023
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

Dialogue system uses natural language as a medium to achieve friendly communication between humans and machines. The performance of intent detection is crucial to the effectiveness of a task-oriented dialogue system. When developing a task-oriented dialogue system in a specific domain, users typically express multiple intents in the same sentence. In this study, we compare two approaches for multi-intent detection at the sentence level, which aims to investigate multi-intent detection for task-oriented dialogue systems based on joint learning. Experiment results on the ATIS and MixATIS datasets show that the multi-classification approach improves slot prediction by combining relevant intent information, whereas the multi-label approach based on joint learning improves intent detection by making predictions at each possible intent.

Citation Download Citation

Qian Zhang, Zesan Liu, Ziheng Wang, and Xiaozhen Li "Multi-intent detection for task-oriented dialogue systems based on joint learning", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125005S (16 December 2022); https://doi.org/10.1117/12.2661023

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