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
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
91–106
Language:
URL:
https://aclanthology.org/Q18-1007
DOI:
10.1162/tacl_a_00007
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/Q18-1007.pdf
Video:
 https://vimeo.com/276372446