Open AccessKorean TableQA: Structured data question answering based on span prediction style with S 3 ‐NET
Author(s) -
Park Cheoneum,
Kim Myungji,
Park Soyoon,
Lim Seungyoung,
Lee Jooyoul,
Lee Changki
Publication year - 2020
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2019-0189
Subject(s) - span (engineering) , net (polyhedron) , question answering , style (visual arts) , computer science , artificial intelligence , natural language processing , mathematics , engineering , geography , structural engineering , geometry , archaeology
The data in tables are accurate and rich in information, which facilitates the performance of information extraction and question answering (QA) tasks. TableQA, which is based on tables, solves problems by understanding the table structure and searching for answers to questions. In this paper, we introduce both novice and intermediate Korean TableQA tasks that involve deducing the answer to a question from structured tabular data and using it to build a question answering pair. To solve Korean TableQA tasks, we use S 3 ‐NET, which has shown a good performance in machine reading comprehension (MRC), and propose a method of converting structured tabular data into a record format suitable for MRC. Our experimental results show that the proposed method outperforms a baseline in both the novice task (exact match (EM) 96.48% and F1 97.06%) and intermediate task (EM 99.30% and F1 99.55%).