SciMRC: Multi-perspective Scientific Machine Reading Comprehension

Xiao Zhang, Heqi Zheng, Yuxiang Nie, Heyan Huang, Xian-Ling Mao


Abstract
Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks.
Anthology ID:
2024.lrec-main.1257
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
14418–14428
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.lrec-main.1257/
DOI:
Bibkey:
Cite (ACL):
Xiao Zhang, Heqi Zheng, Yuxiang Nie, Heyan Huang, and Xian-Ling Mao. 2024. SciMRC: Multi-perspective Scientific Machine Reading Comprehension. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14418–14428, Torino, Italia. ELRA and ICCL.
Cite (Informal):
SciMRC: Multi-perspective Scientific Machine Reading Comprehension (Zhang et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.lrec-main.1257.pdf