Is getting the right answer just about choosing the right words? The role of syntactically-informed features in short answer scoring
This work addresses the need for efficient and scalable automated grading in education, though it appears incremental by building on existing shared task methods.
The paper tackles the problem of automatically scoring short answer questions by exploring the role of syntactically-informed features, aiming to avoid the manual effort of knowledge engineering approaches while leveraging the largest available corpus for this task.
Developments in the educational landscape have spurred greater interest in the problem of automatically scoring short answer questions. A recent shared task on this topic revealed a fundamental divide in the modeling approaches that have been applied to this problem, with the best-performing systems split between those that employ a knowledge engineering approach and those that almost solely leverage lexical information (as opposed to higher-level syntactic information) in assigning a score to a given response. This paper aims to introduce the NLP community to the largest corpus currently available for short-answer scoring, provide an overview of methods used in the shared task using this data, and explore the extent to which more syntactically-informed features can contribute to the short answer scoring task in a way that avoids the question-specific manual effort of the knowledge engineering approach.