Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification

By Guangfeng Yan, Lu Fan, Qimai Li, 刘晗, Xiaotong Zhang, Xiao-Ming Wu, Albert Y. S. Lam. 2020

99 citations recorded by the source corpus.

User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun. This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unknown intent detection. In particular, we model utterance embeddings with a Gaussian mixture distribution and inject dynamic class semantic information into Gaussian means, which enables learning more class-concentrated embeddings that help to facilitate downstream outlier detection. Coupled with a density-based outlier detection algorithm, SEG achieves competitive results on three real task-oriented dialogue datasets in two languages for unknown intent detection. On top of that, we propose to integrate SEG as an unknown intent identifier into existing generalized zero-shot intent classification models to improve their performance. A case study on a state-of-the-art method, ReCapsNet, shows that SEG can push the classification performance to a significantly higher level.

Subfields: Artificial Intelligence, Epidemiology, Ocean Engineering

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In Artificial Intelligence

1. Kang Liu
2. Jun Zhao
3. Shizhu He
4. Heike Adel
5. Jannik Strotgen
6. Qingbin Liu
7. Xiaoyan Yu
8. Dietrich Klakow

Citations in the corpus

• Lifelong Intent Detection via Multi-Strategy Rebalancing (2021, background): “Intent Detection (ID) aims to accurately understand the user intent from a user utterance to guide downstream dialogue policy decisions [5, 10, 25].”
• A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2020, methodology): “The automatic annotation ranges from simple string matching (Yang et al., 2018) to complex pipelines including classifiers and manual steps (Norman et al., 2019).”