| Challenge: | Existing methods for stance detection are not diversified or inconsistent with the given target and label information. |
| Approach: | They propose to augment a text with a conditional masked word prediction task . they propose to replace a target mention with 'target-aware' sentences by replacing a reference word with . |
| Outcome: | The proposed method outperforms existing methods on 11 targets. |
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Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot stance detection are labor-intensive to train for each new target. |
| Approach: | They propose a generative data augmentation approach to generate training samples containing unseen and seen targets and map them into the same embedding space with contrastive learning. |
| Outcome: | The proposed model achieves state-of-the-art on most topics in the task of zero-shot stance detection. |
EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)
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| Challenge: | Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data. |
| Approach: | They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. |
| Outcome: | The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning. |
Cross-Domain Label-Adaptive Stance Detection (2021.emnlp-main)
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
| Approach: | They propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. |
| Outcome: | The proposed framework shows that it can be used to predict unseen labels over strong baselines. |
Stanceformer: Target-Aware Transformer for Stance Detection (2024.findings-emnlp)
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| Challenge: | Existing transformer models that lack the capability to prioritize targets under-perform and are underperforming the task. |
| Approach: | They propose a target-aware transformer model that incorporates enhanced attention towards the targets during both training and inference. |
| Outcome: | The proposed model improves on state-of-the-art models and Large Language Models and can be used for other domains. |
Guiding Computational Stance Detection with Expanded Stance Triangle Framework (2023.acl-long)
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| Challenge: | Experimental results show that strategically-enriched data can significantly improve the performance on out-of-domain and cross-target evaluation. |
| Approach: | They propose to decompose a stance detection task from a theoretical perspective and extend it with additional annotations. |
| Outcome: | The proposed task improves performance on out-of-domain and cross-target evaluations using a linguistic framework. |
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings (2023.emnlp-main)
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| Challenge: | Recent studies have focused on topic-specific stance classifiers that fail to generalize to unseen topics. |
| Approach: | They propose to use contrastive learning and an unlabeled dataset to train topic-agnostic/TAG and topic-aligned/TAW embeddings for use in downstream stance detection. |
| Outcome: | The proposed model achieves state-of-the-art performance across several public stance detection datasets (0.771 F1-score on the Zero-shot VAST dataset). |
Knowledge Enhanced Masked Language Model for Stance Detection (2021.naacl-main)
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| Challenge: | Detecting stance on Twitter is difficult because of the short length of each tweet . Twitter content is dynamic, constantly coining new terminology and hashtags . |
| Approach: | They propose a BERT-based fine-tuning method that enhances stance detection models . they use weighted log-odds-ratio to identify words with high stance distinguishability . |
| Outcome: | The proposed method outperforms the state-of-the-art for stance detection on Twitter data about the 2020 US presidential election. |
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. |
SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection (2022.coling-1)
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| Challenge: | Existing methods for stance detection are task-agnostic, which fail to utilize task knowledge to better discriminate between genuine and bias features. |
| Approach: | They propose to incorporate stance reasoning process as task knowledge to aid in learning genuine features without using targets. |
| Outcome: | The proposed model achieves better performance than previous task-agnostic debiasing methods on new test sets. |
A New Direction in Stance Detection: Target-Stance Extraction in the Wild (2023.acl-long)
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| Challenge: | Existing methods for stance detection assume that the target is known in advance . Existing tasks use implicit mentions in the source text and are infeasible to have manual annotations at a large scale. |
| Approach: | They propose a task Target-Stance Extraction that aims to extract the (target, stance) pair from social media texts. |
| Outcome: | The proposed task can facilitate future research in the field of stance detection. |