mlsanityData to 7 Oct 2026

A Multi-Level Attention Model for Evidence-Based Fact Checking

arXiv 2106.00950 ↗First posted 2 Jun 2021cs.CLcited by 13

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Abstract (arXiv)

Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources. Learning a representation that effectively captures relations between a claim and evidence can be challenging. Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. We present a simple model that can be trained on sequence structures. Our model enables inter-sentence attentions at different levels and can benefit from joint training. Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graph-based approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 2 Jun 2021.

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29 of 46 references.

References · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2017Graph Attention Networkscited by 8,854
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  6. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  7. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  8. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  9. 2018Self-Attention with Relative Position Representationscited by 1,112
  10. 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 896
  11. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  12. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
17 more
  1. 2020Fact or Fiction: Verifying Scientific Claimscited by 546
  2. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 361
  3. 2016Enhanced LSTM for Natural Language Inferencecited by 199
  4. 2018Combining Fact Extraction and Verification with Neural Semantic Matching Networkscited by 120
  5. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  6. 2019GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verificationcited by 100
  7. 2020Coreferential Reasoning Learning for Language Representationcited by 80
  8. 2019Reasoning Over Semantic-Level Graph for Fact Checkingcited by 69
  9. 2018UKP-Athene: Multi-Sentence Textual Entailment for Claim Verificationcited by 60
  10. 2019BERT for Evidence Retrieval and Claim Verificationcited by 57
  11. 2019Revealing the Importance of Semantic Retrieval for Machine Reading at Scalecited by 50
  12. 2018Modeling Localness for Self-Attention Networkscited by 43
  13. 2020A Paragraph-level Multi-task Learning Model for Scientific Fact-Verificationcited by 29
  14. 2020DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checkingcited by 26
  15. 2018TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verificationcited by 17
  16. 2019Team Papelo: Transformer Networks at FEVERcited by 11
  17. 2020Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verificationcited by 11

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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 2; 2025: 3.

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  1. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  2. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  3. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  4. 2022Synthetic Disinformation Attacks on Automated Fact Verification Systemscited by 44
  5. 2023EX-FEVER: A Dataset for Multi-hop Explainable Fact Verificationcited by 36
  6. 2025BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checkingcited by 8
  7. 2025Adversarial Attacks Against Automated Fact-Checking: A Surveycited by 5
  8. 2023XFEVER: Exploring Fact Verification across Languagescited by 4
  9. 2022IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verificationcited by 3
  10. 2022Robust Deepfake On Unrestricted Media: Generation And Detectioncited by 2
  11. 2024Multi-Evidence based Fact Verification via A Confidential Graph Neural Networkcited by 1
  12. 2025Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progressioncited by 1
1 more
  1. 2024Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Modelcited by 0