Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing (2022)

Abstract: Table fact verification aims to check the correctness of textual statements based on given semi-structured data. Most existing methods are devoted to better comprehending logical operations and tables, but they hardly study generating latent programs from statements, with which we can not only retrieve evidences efficiently but also explain reasons behind verifications naturally. However, it is challenging to get correct programs with existing weakly supervised semantic parsers due to the huge search space with lots of spurious programs. In this paper, we address the challenge by leveraging both lexical features and structure features for program generation. Through analyzing the connection between the program tree and the dependency tree, we define a unified concept, operation-oriented tree, to mine structure features, and introduce Structure-Aware Semantic Parsing to integrate structure features into program generation. Moreover, we design a refined objective function with lexical features and violation punishments to further avoid spurious programs. Experimental results show that our proposed method generates programs more accurately than existing semantic parsers, and achieves comparable performance to the SOTA on the large-scale benchmark TABFACT.

## introduction with the rise of misleading information on the internet, such as fake news, rumors and political deceit, fact-checking has been developed as a means of detecting and filtering false information. table fact verification (tfv) is a specific fact-checking task that requires performing logical operations such as comparison, superlative and aggregation over given tables to verify textual statements. programs play an important role in tfv. on one hand, correct programs can provide rationales for model decisions, which make reasoning analysis table statement in 1981 season, the highest number of podiums was 8 and the lowest was 0. and v2:eq v4:eq v1:max 8 v3:min 0 v0:filter_eq podiums all_rows season 1981 program program generation v0 the table where column season equal to 1981 is [row1, row2]. v1 the max value of podiums in [row1, row2] is 8. v2 8 is equal to 8. ## model structure-aware semantic parsing (sasp) centers around the operation-oriented tree to deconstruct some compositionality of statement and generate program correctly. figure 3 gives an overview of our proposed sasp. in this section, we will first introduce the task formulation, then describe how to construct the operation-oriented tree, and give the way to generate programs following the welldesigned tree at last. ## conclusion in this paper, we have proposed a novel approach to do explainable verification by structure-aware semantic parsing. firstly, we define a unified operation-oriented tree by entity linking, dependency parsing and tree pruning. then, we demonstrate how to integrate our proposed tree into semantic parsing with the operator-related and the operand-related principles. at last, we introduce the refined objective function which could reduce the influence of spurious programs. experimental results confirm that our proposed method can bias program generation from label consistent towards semantic consistent and achieve acceptable performance on the benchmark dataset tabfact. future work will collect evidences that are more precise and get better verification performance by replacing lpa with sasp in the first stage of mainstream methods. hai ye, wenjie li, and lu wang. 2019. jointly learning semantic parser and natural language generator via dual information maximization. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 2090-2101. pengcheng yin, graham neubig, wen-tau yih, and sebastian riedel. 2020. tabert: pretraining for joint understanding of textual and tabular data. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 8413-8426. hongzhi zhang, yingyao wang, sirui wang, xuezhi cao, fuzheng zhang, and zhongyuan wang. 2020a. table fact verification with structure-aware transformer. in proceedings of the 2020 conference on empirical methods in natural language processing, pages 1624-1629. yu zhang, zhenghua li, and min zhang. 2020b. efficient second-order treecrf for neural dependency parsing. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 3295-3305. victor zhong, caiming xiong, and richard socher. 2017. seq2sql: generating structured queries from natural language using reinforcement learning. arxiv preprint arxiv:1709.00103. wanjun zhong, duyu tang, zhangyin feng, nan duan, ming zhou, ming gong, linjun shou, daxin jiang, jiahai wang, and jian yin. 2020a. logicalfactchecker: leveraging logical operations for fact checking with graph module network. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 6053-6065. wanjun zhong, jingjing xu, duyu tang, zenan xu, nan duan, ming zhou, jiahai wang, and jian yin. 2020b. reasoning over semantic-level graph for fact checking. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 6170-6180. mantong zhou, minlie huang, and xiaoyan zhu. 2018. an interpretable reasoning network for multi-relation question answering. in proceedings of the 27th international conference on computational linguistics, pages 2010-2022.