Korean-Specific Dataset for Table Question Answering

Changwook Jun, Jooyoung Choi, Myoseop Sim, Hyun Kim, Hansol Jang, Kyungkoo Min


Abstract
Existing question answering systems mainly focus on dealing with text data. However, much of the data produced daily is stored in the form of tables that can be found in documents and relational databases, or on the web. To solve the task of question answering over tables, there exist many datasets for table question answering written in English, but few Korean datasets. In this paper, we demonstrate how we construct Korean-specific datasets for table question answering: Korean tabular dataset is a collection of 1.4M tables with corresponding descriptions for unsupervised pre-training language models. Korean table question answering corpus consists of 70k pairs of questions and answers created by crowd-sourced workers. Subsequently, we then build a pre-trained language model based on Transformer and fine-tune the model for table question answering with these datasets. We then report the evaluation results of our model. We make our datasets publicly available via our GitHub repository and hope that those datasets will help further studies for question answering over tables, and for the transformation of table formats.
Anthology ID:
2022.lrec-1.657
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6114–6120
Language:
URL:
https://aclanthology.org/2022.lrec-1.657
DOI:
Bibkey:
Cite (ACL):
Changwook Jun, Jooyoung Choi, Myoseop Sim, Hyun Kim, Hansol Jang, and Kyungkoo Min. 2022. Korean-Specific Dataset for Table Question Answering. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6114–6120, Marseille, France. European Language Resources Association.
Cite (Informal):
Korean-Specific Dataset for Table Question Answering (Jun et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.657.pdf
Code
 lg-nlp/korwikitablequestions
Data
Korean Table Question AnsweringSQuADWikiTableQuestions