GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)
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| Challenge: | Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence. |
| Approach: | They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence. |
| Outcome: | The proposed framework achieves significant performance improvements on a large-scale benchmark dataset. |
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Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)
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| Challenge: | Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences. |
| Approach: | They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure . |
| Outcome: | The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset. |
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification (2021.emnlp-main)
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| Challenge: | Existing methods leverage programs that contain rich logical information to enhance the verification process. |
| Approach: | They propose a table-based fact verification task as an evidence retrieval framework . they retrieve logic-level program-like evidence from the given table and a statement as supplementary evidence for the table . |
| Outcome: | The proposed method is able to retrieve logic-level program-like evidence from a table and a statement as supplementary evidence for the table. |
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. |
| Approach: | They propose a simple model that can be trained on sequence structures and can benefit from joint training. |
| Outcome: | The proposed model outperforms the graph-based models on a large-scale dataset for Fact Extraction and VERification. |
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification (2022.coling-1)
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| Challenge: | Existing fact verification models lack fine-grained reasoning over key entities . GLAF uses local fission reasoning to capture latent logical relations between clues . |
| Approach: | They propose a global-to-local fission and fissional network to capture latent logical relations hidden in multiple evidence clues. |
| Outcome: | The proposed network achieves state-of-the-art on a FEVER dataset with a 77.62% FEVER score. |
Exploring Listwise Evidence Reasoning with T5 for Fact Verification (2021.acl-short)
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| Challenge: | Existing methods for fact verification use pretrained sequence-to-sequence transformers for sentence selection and label prediction. |
| Approach: | They propose a framework for fact verification that leverages pretrained sequence-to-sequence transformer models for sentence selection and label prediction. |
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Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables (2022.naacl-main)
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| Challenge: | Existing fact extraction and verification tasks only consider evidence of a single format . Existing models convert evidence into either sentences or tables, thus losing context information . |
| Approach: | They propose a Dual Channel Unified Format fact verification model which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously. |
| Outcome: | The proposed model outperforms existing models in two formats by a large margin . it makes the most of existing tables and tables to absorb evidence of two formats . |
UnifEE: Unified Evidence Extraction for Fact Verification (2023.eacl-main)
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| Challenge: | Existing models extract evidence in both sentences and table cells from Wikipedia dumps, ignoring potential connections between them. |
| Approach: | They propose a model which uses a mixed evidence graph to extract the evidence in both formats without manually designed conversion rules. |
| Outcome: | The proposed model outperforms existing models and improves the verification step. |
Enhancing Structured Evidence Extraction for Fact Verification (2023.emnlp-main)
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| Challenge: | Open-domain fact verification requires extracting and integrating both structured and unstructured evidence to verify a claim. |
| Approach: | They propose a method to enhance the extraction of structured evidence by leveraging the row and column semantics of tables. |
| Outcome: | The proposed method achieves evidence recall of 60.01% on the test set, higher than the previous state-of-the-art method. |
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
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Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| Challenge: | Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts. |
| Approach: | They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load. |
| Outcome: | The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content. |
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. |
| Outcome: | The proposed model achieves evidence retrieval, multi-modal claim verification, and explanation generation. |