# SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)

arXiv: https://arxiv.org/abs/2105.13995 · HTML: https://mlsanity.com/paper/2105.13995/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [INFOTABS: Inference on Tables as Semi-structured Data](https://mlsanity.com/paper/2005.06117/) (2020)
- [SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums](https://mlsanity.com/paper/1906.01727/) (2019)
- [TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training](https://mlsanity.com/paper/2104.01099/) (2021)
- [BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with Tables](https://mlsanity.com/paper/2104.03071/) (2021)

## Built on by (most cited first, 30 of 35)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [FactKG: Fact Verification via Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2305.06590/) (2023)
- [SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables](https://mlsanity.com/paper/2305.13186/) (2023)
- [PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training](https://mlsanity.com/paper/2211.02816/) (2022)
- [Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking](https://mlsanity.com/paper/2301.11843/) (2023)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024)
- [CABINET: Content Relevance based Noise Reduction for Table Question Answering](https://mlsanity.com/paper/2402.01155/) (2024)
- [GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering](https://mlsanity.com/paper/2412.01230/) (2024)
- [Table-based Fact Verification with Self-adaptive Mixture of Experts](https://mlsanity.com/paper/2204.08753/) (2022)
- [CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?](https://mlsanity.com/paper/2503.21717/) (2025)
- [ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs](https://mlsanity.com/paper/2403.09724/) (2024)
- [SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2505.10740/) (2025)
- [TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning](https://mlsanity.com/paper/2409.11724/) (2024)
- [Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges](https://mlsanity.com/paper/2508.00217/) (2025)
- [Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning](https://mlsanity.com/paper/2108.00578/) (2021)
- [Knowledge-Aware Reasoning over Multimodal Semi-structured Tables](https://mlsanity.com/paper/2408.13860/) (2024)
- [MuSciClaims: Multimodal Scientific Claim Verification](https://mlsanity.com/paper/2506.04585/) (2025)
- [SciClaimEval: Cross-modal Claim Verification in Scientific Papers](https://mlsanity.com/paper/2602.07621/) (2026)
- [Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework](https://mlsanity.com/paper/2212.10097/) (2022)
- [Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets](https://mlsanity.com/paper/2311.08662/) (2023)
- [Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification](https://mlsanity.com/paper/2109.06480/) (2021)
- [Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers](https://mlsanity.com/paper/2506.10486/) (2025)
- [Leveraging Data Recasting to Enhance Tabular Reasoning](https://mlsanity.com/paper/2211.12641/) (2022)
- [INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification](https://mlsanity.com/paper/2303.01510/) (2023)
- [EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records](https://mlsanity.com/paper/2406.16341/) (2024)
- [Task-Oriented Automatic Fact-Checking with Frame-Semantics](https://mlsanity.com/paper/2501.13288/) (2025)
- [ClaimDB: A Fact Verification Benchmark over Large Structured Data](https://mlsanity.com/paper/2601.14698/) (2026)
- [TabPert: An Effective Platform for Tabular Perturbation](https://mlsanity.com/paper/2108.00603/) (2021)
- [What to Keep and What to Drop: Adaptive Table Filtering Framework](https://mlsanity.com/paper/2506.23463/) (2025)

## Abstract (arXiv)

Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.