ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension

Tanik Saikh, Asif Ekbal, Pushpak Bhattacharyya


Abstract
We present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31%. The framework would be useful for building Question-Answering (QA) systems on scientific articles.
Anthology ID:
2020.lrec-1.675
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
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, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5498–5504
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.675
DOI:
Bibkey:
Cite (ACL):
Tanik Saikh, Asif Ekbal, and Pushpak Bhattacharyya. 2020. ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5498–5504, Marseille, France. European Language Resources Association.
Cite (Informal):
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension (Saikh et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.675.pdf