  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.
