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://aclanthology.org/2024.lrec-main.1257
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1257.pdf