Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection (D19-1)
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| Challenge: | Existing methods for detecting fake news use shared features as complementarity features without selection. |
| Approach: | They propose a sifted multi-task learning method with a selected sharing layer for fake news detection. |
| Outcome: | The proposed method boosts the F1-score by more than 0.87%, 1.31% on two public and widely used competition datasets. |
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| Challenge: | Existing approaches to multi-task learning suffer from the interference between tasks because they lack selection mechanism for feature sharing. |
| Approach: | They propose a multi-task convolutional neural network with the Leaky Unit which has memory and forgetting mechanism to filter the feature flows between tasks. |
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Gated Multi-Task Network for Text Classification (N18-2)
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| Challenge: | Existing approaches to multitask learning share the features without distinguishing the usefulness of the features, generating undesired interference between tasks. |
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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. |
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Entity-Aware Dual Co-Attention Network for Fake News Detection (2023.findings-eacl)
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| Challenge: | Existing models for fake news detection are limited in their ability to detect it from different aspects. |
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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. |
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Localization of Fake News Detection via Multitask Transfer Learning (2020.lrec-1)
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| Challenge: | Existing methods for detecting fake news require large labeled datasets and expert-curated corpora, which low-resource languages may not have. |
| Approach: | They construct a benchmark dataset for fake news detection in Filipino using curated corpora and transfer learning techniques. |
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Neural Multi-Task Learning for Stance Prediction (D19-66)
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| 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. |
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Learning Prototype Representations Across Few-Shot Tasks for Event Detection (2021.emnlp-main)
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| Challenge: | Existing training data for event detection are too expensive to achieve in real applications where novel event types emerge . Typical ED systems require labeled data for each predefined event type, but only a few examples are available. |
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Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors (2023.findings-acl)
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| Challenge: | Existing frameworks for detecting fake news videos are limited . a new approach is proposed to integrate neighborhood information of new videos . |
| Approach: | They propose a framework for automatically detecting fake news videos . it integrates neighborhood relationship of new videos belonging to same event . |
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Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection (2025.findings-acl)
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| Challenge: | Existing models for fake news detection capture domain-shared semantic features but fail to generalize well due to poor adaptability. |
| Approach: | They propose a framework to enable structure knowledge transfer between multiple domains . they compare content-only and propagation-rich data to preserve structural patterns . |
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