@inproceedings{kweon-etal-2023-open,
title = "Open-{W}iki{T}able : Dataset for Open Domain Question Answering with Complex Reasoning over Table",
author = "Kweon, Sunjun and
Kwon, Yeonsu and
Cho, Seonhee and
Jo, Yohan and
Choi, Edward",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.526",
doi = "10.18653/v1/2023.findings-acl.526",
pages = "8285--8297",
abstract = "Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset is publicly available.",
}
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<abstract>Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset is publicly available.</abstract>
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%0 Conference Proceedings
%T Open-WikiTable : Dataset for Open Domain Question Answering with Complex Reasoning over Table
%A Kweon, Sunjun
%A Kwon, Yeonsu
%A Cho, Seonhee
%A Jo, Yohan
%A Choi, Edward
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kweon-etal-2023-open
%X Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset is publicly available.
%R 10.18653/v1/2023.findings-acl.526
%U https://aclanthology.org/2023.findings-acl.526
%U https://doi.org/10.18653/v1/2023.findings-acl.526
%P 8285-8297
Markdown (Informal)
[Open-WikiTable : Dataset for Open Domain Question Answering with Complex Reasoning over Table](https://aclanthology.org/2023.findings-acl.526) (Kweon et al., Findings 2023)
ACL