Papers with few-shot stance detection
Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation (2023.acl-short)
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| Challenge: | Existing work on stance detection focuses on in-domain or leave-out targets with only a few target choices. |
| Approach: | They propose to use a conditional generation framework to denoise from partially-filled templates to better utilize the semantics among input, label, and target texts. |
| Outcome: | The proposed method significantly outperforms strong baselines on VAST and achieves new state-of-the-art performance. |
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. |
LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection (2025.emnlp-main)
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
| Approach: | They propose a framework that uses Large Language Models to annotate implicit targets . they also propose 'DyMCA' to dynamically adjust text-target contributions based on context . |
| Outcome: | The proposed framework achieves state-of-the-art on a benchmark dataset. |
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study (2024.lrec-main)
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| Challenge: | Existing models for stance detection are not robust enough to be used in a viewpoint-diverse news recommender because the news constantly has new discussion topics. |
| Approach: | They propose to use two stance task definitions (Pro/Con versus Same Side Stance) and two LLM architectures (bi-encoding versus cross-encode) to test model performance. |
| Outcome: | The proposed models outperform the same side-stance definition and other models on stance across different topics. |