Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

• URL: http://arxiv.org/abs/2208.08797v1
• Date: Thu, 18 Aug 2022 12:27:24 GMT
• Title: Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
• Authors: Yun Luo, Zihan Liu, Yuefeng Shi, Yue Zhang
• Abstract summary: We propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense.
• Score: 20.620244248582086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset--VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.

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