@inproceedings{liu-etal-2025-augment,
    title = "Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion",
    author = "Liu, Yujian  and
      Ji, Jiabao  and
      Yu, Tong  and
      Rossi, Ryan A.  and
      Kim, Sungchul  and
      Zhao, Handong  and
      Sinha, Ritwik  and
      Zhang, Yang  and
      Chang, Shiyu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1131/",
    doi = "10.18653/v1/2025.findings-emnlp.1131",
    pages = "20769--20786",
    ISBN = "979-8-89176-335-7",
    abstract = "Table question answering is a popular task that assesses a model{'}s ability to understand and interact with structured data. However, the given table often does not contain sufficient information to answer the question, necessitating the integration of external knowledge. Existing methods either convert both the table and external knowledge into text, which neglects the structured nature of the table; or they embed queries for external sources in the interaction with the table, which complicates the process. In this paper, we propose a simple yet effective method to integrate external information in a given table. Our method first constructs an augmenting table containing the missing information and then generates a SQL query over the two tables to answer the question. Experiments show that our method outperforms strong baselines on three table QA benchmarks."
}