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The stance detection task aims to classify the stance toward given documentsand topics. Since the topics can be implicit in documents and unseen intraining data for zero-shot settings, we propose to boost the transferabilityof the stance detection model by using sentiment and commonsense knowledge,which are seldom considered in previous studies. Our model includes a graphautoencoder module to obtain commonsense knowledge and a stance detectionmodule with sentiment and commonsense. Experimental results show that our modeloutperforms the state-of-the-art methods on the zero-shot and few-shotbenchmark dataset--VAST. Meanwhile, ablation studies prove the significance ofeach module in our model. Analysis of the relations between sentiment, commonsense, and stance indicates the effectiveness of sentiment and common sense.

Exploiting Sentiment and Common Sense for Zero-shot Stance Detection