Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

James Fiacco, Shiyan Jiang, David Adamson, Carolyn Rosé


Abstract
Providing effective automatic essay feedback is necessary for offering writing instruction at a massive scale. In particular, feedback for promoting coherent flow of ideas in essays is critical. In this paper we propose a state-of-the-art method for automated analysis of structure and flow of writing, referred to as Rhetorical Structure Theory (RST) parsing. In so doing, we lay a foundation for a generalizable approach to automated writing feedback related to structure and flow. We address challenges in automated rhetorical analysis when applied to student writing and evaluate our novel RST parser model on both a recent student writing dataset and a standard benchmark RST parsing dataset.
Anthology ID:
2022.bea-1.25
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:
204–215
Language:
URL:
https://aclanthology.org/2022.bea-1.25
DOI:
10.18653/v1/2022.bea-1.25
Bibkey:
Cite (ACL):
James Fiacco, Shiyan Jiang, David Adamson, and Carolyn Rosé. 2022. Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 204–215, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation (Fiacco et al., BEA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.25.pdf