@inproceedings{zhang-etal-2024-scimrc-multi,
title = "{S}ci{MRC}: Multi-perspective Scientific Machine Reading Comprehension",
author = "Zhang, Xiao and
Zheng, Heqi and
Nie, Yuxiang and
Huang, Heyan and
Mao, Xian-Ling",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1257",
pages = "14418--14428",
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.",
}
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%0 Conference Proceedings
%T SciMRC: Multi-perspective Scientific Machine Reading Comprehension
%A Zhang, Xiao
%A Zheng, Heqi
%A Nie, Yuxiang
%A Huang, Heyan
%A Mao, Xian-Ling
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhang-etal-2024-scimrc-multi
%X 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.
%U https://aclanthology.org/2024.lrec-main.1257
%P 14418-14428
Markdown (Informal)
[SciMRC: Multi-perspective Scientific Machine Reading Comprehension](https://aclanthology.org/2024.lrec-main.1257) (Zhang et al., LREC-COLING 2024)
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.