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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.675.pdf