Papers › TabFact: A Large-scale Dataset for Table-based Fact Verification

TabFact: A Large-scale Dataset for Table-based Fact Verification

5 Sep 2019ICLR 2020 1arXiv:1909.02164archive 2025-07-28

Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang

The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains under-explored. This paper specifically aims to study the fact verification given semi-structured data as evidence. To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED. TabFact is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA). Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification. LPA parses statements into programs and executes them against the tables to obtain the returned binary value for verification. Both methods achieve similar accuracy but still lag far behind human performance. We also perform a comprehensive analysis to demonstrate great future opportunities. The data and code of the dataset are provided in \url{https://github.com/wenhuchen/Table-Fact-Checking}.

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Tasks

16kFact CheckingFact VerificationLanguage ModellingNatural Language UnderstandingTable-based Fact Verification

Datasets

Introduced by this paper, per the archive.

TabFact

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Table-based Fact Verification TabFact Table-BERT-Horizontal-T+F-Template Test 65.12 #14 of 15 Archive leaderboard report
Table-based Fact Verification TabFact Table-BERT-Horizontal-T+F-Template Val 66.1 #14 of 15 Archive leaderboard report
Table-based Fact Verification TabFact BERT classifier w/o Table Test 50.5 #15 of 15 Archive leaderboard report
Table-based Fact Verification TabFact BERT classifier w/o Table Val 50.9 #15 of 15 Archive leaderboard report

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