Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
This work provides an automated solution for generating multiple-choice question distractors, which can reduce the cost and time for educators and test developers.
This paper addresses the challenge of automatically generating distractors for multiple-choice questions, which are typically crafted by domain experts. The proposed EDGE framework generates distractors that are both incorrect and plausible, significantly outperforming existing models and achieving state-of-the-art results on a large-scale public dataset.
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.