Causal Denoising Prototypical Network for Few-Shot Multi-label Aspect Category Detection

Abstract

The multi-label aspect category detection (MACD) task has attracted great attention in sentiment analysis. Many recent methods have formulated the MACD task by learning robust prototypes to represent categories with limited support samples. However, few of them address the noise categories in the support set that hinder their models from effective prototype generations. To this end, we propose a causal denoising prototypical network (CDPN) for few-shot MACD. We reveal the underlying relation between causal inference and contrastive learning, and present causal contrastive learning (CCL) using discrete and continuous noise as negative samples. We empirically found that CCL can (1) prevent models from overly predicting more categories and (2) mitigate semantic ambiguity issues among categories. Experimental results show that CDPN outperforms competitive baselines. Our code is available online.