| Challenge: | Existing models for fact checking are limited in size due to limited data available . stance detection is a key component of fact checking for journalists and news agencies . |
| Approach: | They propose to use textual information from existing datasets to improve stance prediction. |
| Outcome: | The proposed model outperforms state-of-the-art systems on a public benchmark dataset by 6.0 and 14.4 points in weighting. |
Similar Papers
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. |
All-in-one: Multi-task Learning for Rumour Verification (C18-1)
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| Challenge: | Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline . previous work focused on rumor detection, rumou tracking and stance classification as separate components . |
| Approach: | They propose a multi-task learning approach that allows joint training of main and auxiliary tasks, improving the performance of rumour verification. |
| Outcome: | The proposed approach improves the performance of rumour verification by combining main and auxiliary tasks into one pipeline. |
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
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| Challenge: | Social media platforms do not always pose authentic information, and rumors spread fear or hate. |
| Approach: | They propose a new multi-task learning approach for rumor detection and stance classification tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets. |
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. |
Tribrid: Stance Classification with Neural Inconsistency Detection (2021.emnlp-main)
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| Challenge: | a new neural architecture can be used to classify stances on social media without relying on linguistic features. |
| Approach: | They propose a neural architecture where the input also includes automatically generated negated perspectives over a given claim. |
| Outcome: | The proposed model improves on the original input and removes doubtful predictions over the retained information. |
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation (2021.emnlp-main)
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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 a method that applies instance-specific temperature scaling to the teacher and student predictions. |
| Outcome: | The proposed method outperforms the state-of-the-art on all datasets and on multiple datasets. |
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. |
Improving Multi-task Stance Detection with Multi-task Interaction Network (2022.emnlp-main)
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| Challenge: | Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection but neglect to capture the fine-grained task-specific interaction between stance and sentiment tasks, thus degrading performance. |
| Approach: | They propose a novel multi-task interaction network (MTIN) that captures the word-level interaction between tasks, so as to obtain richer task representations. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two real-world datasets. |
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. |
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 . |