Work

Good Question! Statistical Ranking for Question Generation

Year: 2018

Type: article

Abstract: We address the challenge of automatically generating questions from reading materials for educational practice and assessment. Our approach is to overgenerate questions, then rank them. We use manually written rules to perform a sequence of general purpose syntactic transformations (e.g., subject-auxiliary inversion) to turn declarative sentences into questions. These questions are then ranked by a logistic regression model trained on a small, tailored dataset consisting of labeled output from our system. Experimental results show that ranking nearly doubles the percentage of questions rated as acceptable by annotators, from 27% of all questions to 52% of the top ranked 20% of questions.

DOI: 10.1184/r1/6473390

Authors: Heilman, Michael, A. Smith, Noah

Institution: Carnegie Mellon University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funders: National Science Foundation, U.S. Department of Education, Carnegie Mellon University, Institute of Education Sciences

Awards: G1649919334, RI-Small: Probabilistic Models for Structure Discovery in Text, Program in Interdisciplinary Education Research (PIER)