Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Survey on Table Question Answering: Recent Advances (2207.05270v1)

Published 12 Jul 2022 in cs.CL and cs.AI

Abstract: 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Nengzheng Jin (2 papers)
  2. Joanna Siebert (5 papers)
  3. Dongfang Li (46 papers)
  4. Qingcai Chen (36 papers)
Citations (36)

Summary

We haven't generated a summary for this paper yet.