Don’t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing

Yuning Ding, Marie Bexte, Andrea Horbach


Abstract
In this paper, we explore the role of topic information in student essays from an argument mining perspective. We cluster a recently released corpus through topic modeling into prompts and train argument identification models on different data settings. Results show that, given the same amount of training data, prompt-specific training performs better than cross-prompt training. However, the advantage can be overcome by introducing large amounts of cross-prompt training data.
Anthology ID:
2022.bea-1.17
Volume:
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
BEA | NAACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
124–133
Language:
URL:
https://aclanthology.org/2022.bea-1.17
DOI:
10.18653/v1/2022.bea-1.17
Bibkey:
Cite (ACL):
Yuning Ding, Marie Bexte, and Andrea Horbach. 2022. Don’t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 124–133, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Don’t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing (Ding et al., BEA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.17.pdf
Code
 yuningding/bea-naacl-2022-38