When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing

Yuning Ding, Omid Kashefi, Swapna Somasundaran, Andrea Horbach

Abstract
In this paper, we investigate the role of arguments in the automatic scoring of cohesion in argumentative essays. The feature analysis reveals that in argumentative essays, the lexical cohesion between claims is more important to the overall cohesion, while the evidence is expected to be diverse and divergent. Our results show that combining features related to argument segments and cohesion features improves the performance of the automatic cohesion scoring model trained on a transformer. The cohesion score is also learned more accurately in a multi-task learning process by adding the automatic segmentation of argumentative elements as an auxiliary task. Our findings contribute to both the understanding of cohesion in argumentative writing and the development of automatic feedback.
Anthology ID:
2024.lrec-main.1523
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:
17513–17524
Language:
URL:
https://aclanthology.org/2024.lrec-main.1523
DOI:
Bibkey:
Cite (ACL):
Yuning Ding, Omid Kashefi, Swapna Somasundaran, and Andrea Horbach. 2024. When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17513–17524, Torino, Italia. ELRA and ICCL.
Cite (Informal):
When Argumentation Meets Cohesion: Enhancing Automatic Feedback in Student Writing (Ding et al., LREC-COLING 2024)
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