Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

TL;DR

This paper introduces EDGE, an automated framework for generating plausible distractors for multiple choice questions, reducing reliance on costly human experts and improving test question quality.

Contribution

The paper presents a novel question and answer guided distractor generation framework that outperforms existing models and sets new state-of-the-art results.

Findings

Significantly outperforms existing distractor generation models

Achieves new state-of-the-art performance on a large-scale dataset

Demonstrates effectiveness across various domains

Abstract

To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.