Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
This addresses the problem of stance detection for unseen topics in zero-shot settings, which is incremental as it builds on existing methods by adding new features.
The paper tackled zero-shot stance detection by incorporating sentiment and commonsense knowledge, which are rarely used in prior work, and showed that their model outperforms state-of-the-art methods on the VAST benchmark dataset.
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.