Journal
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/ijcnn48605.2020.9206635
Keywords
stance detection; adversarial domain generalization; transfer learning; attention
Funding
- National Natural Science Foundation of China [61672211, U1836222]
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Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets. In this study, we investigate the domain difference between targets and thus incorporate attention-based conditional encoding with adversarial domain generalization to perform unseen target stance detection. Experimental results show that our approach achieves new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the importance of domain difference between targets in unseen target stance detection.
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