Researches on Question Answering Over Tables:A Survey
Table question answering(Table QA)directly gets answers form table data through natural language,which is one of the main forms of intelligent question answering.Recently,researchers pay great attention to resolve this task by semantic parsing.In this paper,we divide Table QA tasks into three types:single-table single-turn,multi-table single-turn,and multi-table multi-turn.This paper provides a systematic introduction to datasets and representative methods of various types of Table QA tasks.It also summarizes the data construction,input enco-ding,and pre-training objectives of the table pre-training models.Finally,we explore the strengths and weaknesses of current work,and discuss the future prospects and challenges of Table QA.
table question answeringsemantic parsingnatural language processingsurvey
NETL
NSTL
万方数据
