# Exploring Decomposition for Table-based Fact Verification

arXiv: https://arxiv.org/abs/2109.11020 · HTML: https://mlsanity.com/paper/2109.11020/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [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)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Break It Down: A Question Understanding Benchmark](https://mlsanity.com/paper/2001.11770/) (2020)
- [Combining Fact Extraction and Verification with Neural Semantic Matching Networks](https://mlsanity.com/paper/1811.07039/) (2018)
- [Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://mlsanity.com/paper/1906.02916/) (2019)
- [Unsupervised Question Decomposition for Question Answering](https://mlsanity.com/paper/2002.09758/) (2020)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [Assessing The Factual Accuracy of Generated Text](https://mlsanity.com/paper/1905.13322/) (2019)
- [Understanding tables with intermediate pre-training](https://mlsanity.com/paper/2010.00571/) (2020)
- [UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://mlsanity.com/paper/1809.01479/) (2018)
- [Learning To Split and Rephrase From Wikipedia Edit History](https://mlsanity.com/paper/1808.09468/) (2018)
- [Program Enhanced Fact Verification with Verbalization and Graph Attention Network](https://mlsanity.com/paper/2010.03084/) (2020)
- [LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network](https://mlsanity.com/paper/2004.13659/) (2020)
- [Split and Rephrase](https://mlsanity.com/paper/1707.06971/) (2017)
- [Split and Rephrase: Better Evaluation and a Stronger Baseline](https://mlsanity.com/paper/1805.01035/) (2018)
- [Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification](https://mlsanity.com/paper/1910.12203/) (2019)
- [Answering Complicated Question Intents Expressed in Decomposed Question Sequences](https://mlsanity.com/paper/1611.01242/) (2016)
- [Fact-aware Sentence Split and Rephrase with Permutation Invariant Training](https://mlsanity.com/paper/2001.11383/) (2020)

## Built on by (most cited first, 11 of 11)

- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://mlsanity.com/paper/2210.12374/) (2022)
- [MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.05587/) (2025)
- [Table-based Fact Verification with Self-adaptive Mixture of Experts](https://mlsanity.com/paper/2204.08753/) (2022)
- [LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control](https://mlsanity.com/paper/2302.02962/) (2023)
- [POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval](https://mlsanity.com/paper/2505.19189/) (2025)
- [WDV: A Broad Data Verbalisation Dataset Built from Wikidata](https://mlsanity.com/paper/2205.02627/) (2022)
- [Leveraging Data Recasting to Enhance Tabular Reasoning](https://mlsanity.com/paper/2211.12641/) (2022)
- [TabVer: Tabular Fact Verification with Natural Logic](https://mlsanity.com/paper/2411.01093/) (2024)
- [Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats](https://mlsanity.com/paper/2406.17574/) (2024)
- [Analyzing LLM Instruction Optimization for Tabular Fact Verification](https://mlsanity.com/paper/2602.17937/) (2026)

## Abstract (arXiv)

Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trained language models have demonstrated a strong capability in verifying simple statements, they struggle with complex statements that involve multiple operations. In this paper, we improve fact verification by decomposing complex statements into simpler subproblems. Leveraging the programs synthesized by a weakly supervised semantic parser, we propose a program-guided approach to constructing a pseudo dataset for decomposition model training. The subproblems, together with their predicted answers, serve as the intermediate evidence to enhance our fact verification model. Experiments show that our proposed approach achieves the new state-of-the-art performance, an 82.7% accuracy, on the TabFact benchmark.