arXiv:2204.03839
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Infusing Knowledge from Wikipedia to Enhance Stance Detection
원문 초록
Stance detection infers a text author's attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.
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Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Few-Shot Stance Detection via Target-Aware Prompt Distillation
Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content
Words That Unite The World: A Unified Framework for Deciphering Central Bank Communications Globally
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