Towards Evaluating Narrative Quality In Student Writing
Swapna Somasundaran, Michael Flor, Martin Chodorow, Hillary Molloy, Binod Gyawali, Laura McCulla
Abstract
This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.- Anthology ID:
- Q18-1007
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 6
- Month:
- Year:
- 2018
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova, Brian Roark
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 91–106
- Language:
- URL:
- https://aclanthology.org/Q18-1007
- DOI:
- 10.1162/tacl_a_00007
- Cite (ACL):
- Swapna Somasundaran, Michael Flor, Martin Chodorow, Hillary Molloy, Binod Gyawali, and Laura McCulla. 2018. Towards Evaluating Narrative Quality In Student Writing. Transactions of the Association for Computational Linguistics, 6:91–106.
- Cite (Informal):
- Towards Evaluating Narrative Quality In Student Writing (Somasundaran et al., TACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q18-1007.pdf