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FalCon: A Faithful Contrastive Framework for Response Generation in TableQA Systems

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13247)

Abstract

In a practical TableQA system, response generation is a critical module to generate a natural language description of the SQL and the execution result. Due to the complex syntax of SQL and matching issues with table content, this task is prone to produce factual errors. In this paper, we propose FalCon, a Faithful Contrastive generation framework to improve the factual correctness of generated responses. FalCon forces the generation model to identify examples with factual errors in the latent space during training and takes contrastive examples into consideration during inference. We also propose two new automatic metrics to further evaluate faithfulness specialized to this task. Experimental results show FalCon brings a favorable performance improvement on both automatic and human evaluation amongst various baseline methods (The code of FalCon is released at https://github.com/whuFSN/FalCon).

Keywords

  • Response generation
  • Factual correctness
  • Contrastive learning

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Notes

  1. 1.

    Synchronously modify headers and values in the result table.

  2. 2.

    We only use the first three rules for constructing source imposters when inference.

  3. 3.

    We report the average best performance observed in 3 runs on the development set of CoSQL since its test set are not public. All improvements of FalCon are significant with \(p<0.01\) compared to backbone models.

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Acknowledgements

We thank anonymous reviewers for their comments and suggestions. This work was supported by National Key Research and Development Project (No. 2020AAA0109302), Shanghai Science and Technology Innovation Action Plan (No. 19511120400) and Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX0103).

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Correspondence to Yanghua Xiao .

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Fang, S., Chen, J., Shen, X., Chen, Y., Xiao, Y. (2022). FalCon: A Faithful Contrastive Framework for Response Generation in TableQA Systems. In: , et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_13

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