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
- Editors:
- Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
- Venue:
- BEA
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.bea-1.17.pdf
- Code
- yuningding/bea-naacl-2022-38