Multi-Label Few-Shot Learning for Aspect Category Detection

• URL: http://arxiv.org/abs/2105.14174v1
• Date: Sat, 29 May 2021 01:56:11 GMT
• Title: Multi-Label Few-Shot Learning for Aspect Category Detection
• Authors: Mengting Hu, Shiwan Zhao, Honglei Guo, Chao Xue, Hang Gao, Tiegang Gao, Renhong Cheng, Zhong Su
• Abstract summary: Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. Existing few-shot learning approaches mainly focus on single-label predictions. We propose a multi-label few-shot learning method based on the prototypical network.
• Score: 23.92900196246631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD task since a sentence may contain multiple aspect categories. Therefore, we propose a multi-label few-shot learning method based on the prototypical network. To alleviate the noise, we design two effective attention mechanisms. The support-set attention aims to extract better prototypes by removing irrelevant aspects. The query-set attention computes multiple prototype-specific representations for each query instance, which are then used to compute accurate distances with the corresponding prototypes. To achieve multi-label inference, we further learn a dynamic threshold per instance by a policy network. Extensive experimental results on three datasets demonstrate that the proposed method significantly outperforms strong baselines.

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