DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020.acl-main)
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| Challenge: | Recent methods to discover evidence for explainable claim verification are nontransparent and unexplained. |
| Approach: | They propose a Decision Tree-based Co-Attention model to discover evidence for explainable claim verification using neural networks. |
| Outcome: | The proposed model boosts the F1-score by more than 3.11%, 2.41% on two public datasets. |
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| Challenge: | Claim verification is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence. |
| Approach: | They propose an end-to-end hierarchical attention network that learns to represent coherent evidence and their semantic relatedness with the claim. |
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GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020.acl-main)
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| Challenge: | Existing methods to detect fake news on social media are based on textual features and advanced linguistic features. |
| Approach: | They propose a neural network-based model to detect fake news on social media . they use a short-text tweet and a sequence of retweets without text comments to predict whether the source tweet is fake or not. |
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A Multi-Level Attention Model for Evidence-Based Fact Checking (2021.findings-acl)
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| Challenge: | Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. |
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ART: Adaptive Reasoning Trees for Explainable Claim Verification (2026.findings-eacl)
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Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, Abbaas Alif Mohamed Nishar, Pranab Mohanty, Swapnil Shinde, Yue Wu
| Challenge: | Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. |
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Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (2020.coling-main)
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| Challenge: | Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. |
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MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval (2026.eacl-long)
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| Challenge: | Existing methods for verification of claims rely on textual evidence only or ignore the explainability. |
| Approach: | They propose a multi-modal reasoning model that integrates text and visual evidence for verification. |
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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition (D18-1)
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Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, Dongyu Zhang
| Challenge: | Homographic puns have a long history in human writing, widely used in written and spoken literature, which intended as jokes. |
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Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020.emnlp-main)
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| Challenge: | Existing methods for fact verification based on structured data are challenging and require further study. |
| Approach: | They propose a program-enhanced verbalization and a graph attention network to integrate programs and execution into textual inference models. |
| Outcome: | The proposed framework achieves a new state-of-the-art accuracy on a benchmark dataset . it is compared with existing frameworks on symbolic and informal inference models . |
FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information (2024.lrec-main)
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| Challenge: | Existing evidence-based fact-checking efforts are time-consuming and challenging . however, relying on surface patterns of claims makes it difficult to identify subtle connections between claims and evidence. |
| Approach: | They propose a model that leverages sentence-level attention and graph attention network to enhance accuracy and fusing claims and evidence information for accurate identification of even well-disguised data. |
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Unified Dual-view Cognitive Model for Interpretable Claim Verification (2021.acl-long)
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| Challenge: | Existing studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. |
| Approach: | They propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) that captures word-level semantics based . on individual cognition, they adjust the proportion between them to generate global evidence. |
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