Augment before You Try: Knowledge-Enhanced Table Question Answering via
  Table Expansion
Yujian Liu Jiabao Ji Tong Yu Ryan Rossi Sungchul Kim Handong Zhao Ritwik Sinha Yang Zhang Shiyu Chang
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 for answering 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. Our code is publicly available at
https://github.com/UCSB-NLP-Chang/Augment_tableQA.