TableQA: Question Answering on Tabular Data

Vakulenko, Svitlana; Savenkov, Vadim

doi:10.48550/arxiv.1705.06504

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2021

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“…A fast-growing area is QA based on information from tables. At least four resources are based on Wikipedia tabular data, including WikiTableQuestions [195] and TableQA [260]. Two of them have supporting annotations for attention supervision: SQL queries in WikiSQL [290], operand information in WikiOps [49].…”

Section: Semi-structured Text (Tables)mentioning

confidence: 99%

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Rogers¹,

Gardner²,

Augenstein³

2021

Preprint

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Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress. Question answering and reading comprehension have been particularly prolific in this regard, with over 80 new datasets appearing in the past two years. This study is the largest survey of the field to date. We provide an overview of the various formats and domains of the current resources, highlighting the current lacunae for future work. We further discuss the current classifications of "reasoning types" in question answering and propose a new taxonomy. We also discuss the implications of over-focusing on English,

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Section: Semi-structured Text (Tables)mentioning

confidence: 99%