Towards Evaluating Narrative Quality In Student Writing
Author(s) -
Swapna Somasundaran,
Michael Flor,
Martin Chodorow,
Hillary Molloy,
Binod Gyawali,
Laura McCulla
Publication year - 2018
Publication title -
transactions of the association for computational linguistics
Language(s) - English
Resource type - Journals
ISSN - 2307-387X
DOI - 10.1162/tacl_a_00007
Subject(s) - narrative , computer science , quality (philosophy) , set (abstract data type) , foundation (evidence) , natural language processing , feature (linguistics) , writing assessment , measure (data warehouse) , artificial intelligence , linguistics , mathematics education , psychology , data mining , history , programming language , philosophy , archaeology , epistemology
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom