| Challenge: | Recent studies have focused on document-level opinion mining, but linguistic information is correlated with the stance of the document. |
| Approach: | They propose a hierarchical attention neural model to employ various linguistic information to construct the document representation. |
| Outcome: | The proposed model can detect stance of documents on two datasets. |
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Multi-Task Stance Detection with Sentiment and Stance Lexicons (D19-1)
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| Challenge: | Recent studies show improvements in stance detection by using attention mechanism or sentiment information. |
| Approach: | They propose a multi-task framework that incorporates attention mechanism and takes sentiment classification as an auxiliary task. |
| Outcome: | The proposed model outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset. |
Cross-Target Stance Classification with Self-Attention Networks (P18-2)
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| Challenge: | In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. |
| Approach: | They propose a neural model that can generalize classifiers between different targets by finding useful information shared between relevant targets. |
| Outcome: | The proposed model can generalize between relevant targets and find useful information shared between relevant target domains which improves generalization in certain scenarios. |
Contrastive Language Adaptation for Cross-Lingual Stance Detection (D19-1)
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| Challenge: | Current approaches to fact-checking are time-consuming and tedious. |
| Approach: | They propose a novel approach which leverages labeled data in one language to identify relative perspective of a document with respect to a claim in a different target language. |
| Outcome: | The proposed approach can deal with the challenge of limited labeled data in the target language. |
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. |
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. |
Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023.findings-acl)
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| Challenge: | Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages. |
| Approach: | They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations. |
| Outcome: | The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages. |
Dynamic Stance: Modeling Discussions by Labeling the Interactions (2023.findings-emnlp)
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| Challenge: | Stance detection is a popular task that has been modeled as a static task, but its limitations are strong topic-dependent. |
| Approach: | They propose to model stance as a dynamic task by focusing on interactions between a message and their replies. |
| Outcome: | The proposed model shows portability across topics and languages. |
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. |
Tweet Stance Detection Using an Attention based Neural Ensemble Model (N19-1)
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| Challenge: | Existing deep learning approaches to stance detection in twitter are inadequate to deal with the vanishing-gradient and overfitting problems. |
| Approach: | They propose a neural ensemble model that adopts strengths of two LSTM variants to learn better long-term dependencies. |
| Outcome: | The proposed model improves on the existing deep learning models on single and multi-target stance detection datasets. |
Modeling Human-Like Cognition for Stance Detection: Integrating Intuitive Judgment and Analytical Reasoning (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized stance detection, enabling complex reasoning strategies such as chain-of-thought prompting. |
| Approach: | They propose Cognitive-Driven Stance Detection (CDSD) that integrates fast intuitive judgment and analytical reasoning enhanced by three key modules: attention-based cognitive alignment to compare system focus, uncertainty-aware belief update using Bayesian inference, and self-doubt-triggered counterfactual reasoning for re-evaluation under low consistency or high uncertainty. |
| Outcome: | The proposed method outperforms state-of-the-art methods on SEM16, P-Stance, and VAST. |