A Survey on Table Question Answering: Recent Advances
It addresses the lack of overviews in this research area, benefiting researchers and practitioners in natural language processing and data analysis.
This paper provides a comprehensive survey on Table Question Answering, classifying existing methods into five categories and identifying key challenges and future directions.
Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, as table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.