Exploring Decomposition for Table-based Fact Verification (2021)

Abstract: 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.

## introduction fact verification aims to validate if a statement is entailed or refuted by given evidence. it has become crucial to many applications such as detecting fake news and rumor @xcite @xcite @xcite . while existing research mainly focuses on verification based on unstructured text @xcite @xcite , a recent trend is to explore structured data as evidence, which is ubiquitous in our daily life. verification performed with structured data presents research challenges of fundamental interests, as it involves both informal inference based on language understanding and symbolic operations such as mathematical operations (e.g., count and max). while all statements share the same set of operations, complex statements, which involve multiple operations, are more challenging than the firhill venue had the highest attendance. ## experiments setup. we conduct our experiments on a largescale table-based fact verification benchmark tab-fact @xcite . the test set contains a simple and complex subset according to difficulty. a small test set is further annotated with human performance. following the previous work, we use accuracy as the evaluation metric. details of the data are listed in appendix a.3. ## related work existing work on fact verification is mainly based on evidences from unstructured text @xcite @xcite @xcite . our work focuses on fact verification based on structured tables @xcite . unlike the previous work @xcite @xcite @xcite , we propose a framework to verify statements via decomposition. sentence decomposition takes the form of splitand-rephrase proposed by @xcite to split a complex sentence into a sequence of shorter sentences while preserving original meanings @xcite @xcite . in qa task, question decomposition has been applied to help answer multi-hop ques-tions @xcite @xcite @xcite . our work mainly focuses on decomposing statements for table-based fact verification with pseudo supervision from programs. ## conclusion in this paper, we propose a framework to better verify the complex statements via decomposition. without annotating gold decompositions, we propose a program-guided approach to creating pseudo decompositions on which we finetune the gpt-2 for decomposition generation. by solving the decomposed subproblems, we can integrate useful intermediate evidence for final verification and improve the state-of-the-art performance to an 82.7% accuracy on tabfact.