Work

Effective Feature Integration for Automated Short Answer Scoring

Year: 2015

Type: conference-paper

Abstract: A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring).A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity.Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used.Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.3115/v1/n15-1111

Authors: Keisuke Sakaguchi, Michael Heilman, Nitin Madnani

Institutions: Educational Testing Service, Johns Hopkins University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W2293665254