Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

• URL: http://arxiv.org/abs/2010.02481v2
• Date: Sun, 11 Oct 2020 06:07:12 GMT
• Title: Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
• Authors: Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu
• Abstract summary: Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
• Score: 69.2370349274216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components. These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent. In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. These semantic components capture high-level information, resulting in more effective matching between instances. Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances. We also propose a more challenging evaluation setting that considers classification on the joint all-class label space. Extensive experimental results demonstrate the effectiveness of our method. Our code and data are publicly available.

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