2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-11.2
Paper Title BOOSTING LOW-RESOURCE INTENT DETECTION WITH IN-SCOPE PROTOTYPICAL NETWORKS
Authors Hongzhan Lin, Yuanmeng Yan, Guang Chen, Beijing University of Posts and Telecommunications, China
SessionHLT-11: Language Understanding 3: Speech Understanding - General Topics
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Identifying intentions from users can help improve the response quality of task-oriented dialogue systems. How to use only limited labeled in-domain (ID) examples for zero-shot unknown intent detection and few-shot ID classification is a more challenging task in spoken language understanding. Existing related methods heavily rely upon the multi-domain datasets containing large-scale independent source domains for meta-training. In this paper, we propose a universal In-scope Prototypical Networks for low-resource intent detection to be general to dialogue meta-train datasets lacking widely-varying domains, which focuses on the scope of episodic intent classes to construct meta-task dynamically. Also, we introduce loss with margin principle to better distinguish samples. Experiments on two benchmark datasets show that our model consistently outperforms other baselines on zero-shot unknown intent detection without deteriorating the competitive performance on few-shot ID classification.