mlsanityData to 7 Oct 2026

Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification

arXiv 2109.12349 ↗First posted 25 Sep 2021cs.CLcited by 6

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Abstract (arXiv)

This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 25 Sep 2021.

Builds on

23 of 33 references.

References · 23

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2017Graph Attention Networkscited by 8,854
  4. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  5. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 798
  6. 2019Defending Against Neural Fake Newscited by 553
  7. 2020Fact or Fiction: Verifying Scientific Claimscited by 546
  8. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  9. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 209
  10. 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
  11. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  12. 2020GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Mediacited by 108
11 more
  1. 2019Fine-grained Fact Verification with Kernel Graph Attention Networkcited by 105
  2. 2019GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verificationcited by 100
  3. 2020INFOTABS: Inference on Tables as Semi-structured Datacited by 84
  4. 2020Explainable Automated Fact-Checking: A Surveycited by 83
  5. 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
  6. 2019Reasoning Over Semantic-Level Graph for Fact Checkingcited by 69
  7. 2018UKP-Athene: Multi-Sentence Textual Entailment for Claim Verificationcited by 60
  8. 2019Revealing the Importance of Semantic Retrieval for Machine Reading at Scalecited by 50
  9. 2020DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checkingcited by 26
  10. 2019Explainable Fact Checking with Probabilistic Answer Set Programmingcited by 23
  11. 2020Joint Verification and Reranking for Open Fact Checking Over Tablescited by 16

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 4.

6 papers.

  1. 2022Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learningcited by 15
  2. 2022Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Trainingcited by 5
  3. 2024Heterogeneous Graph Reasoning for Fact Checking over Texts and Tablescited by 5
  4. 2024Generating Zero-shot Abstractive Explanations for Rumour Verificationcited by 1
  5. 2024Fact-checking based fake news detection: a reviewcited by 0
  6. 2024Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Modelcited by 0