Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph (2021.findings-acl)
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| Challenge: | Existing methods for stance detection are not applicable to zero-shot and few-shot scenarios. |
| Approach: | They propose a model that integrates commonsense knowledge into a stance detection model. |
| Outcome: | The proposed model outperforms state-of-the-art methods on zero-shot and few-shot stance detection tasks. |
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| Challenge: | Existing stance detection models use sentiment and commonsense knowledge to classify stance toward documents and topics . obtaining rich annotated data in stance detector is time-consuming and laborintensive . |
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| Challenge: | Existing methods for stance detection are topic-specific and cross-target stance. |
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| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
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| Challenge: | Existing methods for stance detection focus on background information and not on the accompanying input texts. |
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
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Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
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Adversarial Learning for Zero-Shot Stance Detection on Social Media (2021.naacl-main)
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| Challenge: | a new model for zero-shot stance detection on Twitter uses adversarial learning to generalize across topics . previous work on zero- shot stance detector on English social media focuses on cross-target stances . |
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
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Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)
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| Challenge: | Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it. |
| Approach: | They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample. |
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