Semantic Matching and Aggregation Network for Few shot Intent Detection
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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