Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu · 2020
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 1 .