| 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. |
Similar Papers
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
| Approach: | They propose to use a large stance detection dataset in the political domain to detect stances on twitter. |
| Outcome: | The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection. |
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. |
Multi-modal Stance Detection: New Datasets and Model (2024.findings-acl)
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| Challenge: | Existing methods for stance detection for pure texts have limited results to multi-modal content. |
| Approach: | They propose a multi-modal stance detection framework that leverages target information to learn multi-modal stance features from textual and visual modalities. |
| Outcome: | The proposed framework achieves state-of-the-art in multi-modal stance detection on five datasets based on Twitter . |
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. |
A Multi-Task Learning Framework for Multi-Target Stance Detection (2021.findings-acl)
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| Challenge: | Existing models fail to learn target-specific representations and are prone to overfitting. |
| Approach: | They propose a multi-task learning network to train one model on all target pairs . their results show that their proposed model outperforms the best-performing baseline by 12.39% . |
| Outcome: | The proposed model outperforms the best-performing baseline model by 12.39% in macro-averaged F1-score. |
Stance Detection with Hierarchical Attention Network (C18-1)
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| 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. |
A Few Topical Tweets are Enough for Effective User Stance Detection (2021.eacl-main)
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| Challenge: | Recent work that employs unsupervised classification has shown that user stance detection is highly accurate on vocal Twitter users, but fails for less vocal users, who may have only authored a few tweets about a target. |
| Approach: | They propose to perform user stance detection on vocal Twitter users using contextualized embeddings to represent tweets and then perform unsupervised classification of the user. |
| Outcome: | The proposed method outperforms two baseline methods and achieves 89.6% accuracy and 91.3% macro F-measure on eight controversial topics. |
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets (2021.naacl-main)
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| Challenge: | Existing stance detection datasets are complex deep neural networks, making them difficult to interpret. |
| Approach: | They propose a new large dataset free of such biases and demonstrate its aptness on existing stance detection systems. |
| Outcome: | The proposed model achieves human-level performance on the WT–WT dataset and more than two-third accuracy on other datasets. |
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). |
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 . |
| Approach: | They propose a model that uses adversarial learning to generalize across topics on Twitter . their model achieves state-of-the-art performance on unseen test topics . |
| Outcome: | The proposed model achieves state-of-the-art performance on unseen topics with minimal computational costs. |