  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.
