  In this paper, we propose a novel configurable framework to automatically
generate distractive choices for open-domain cloze-style multiple-choice
questions, which incorporates a general-purpose knowledge base to effectively
create a small distractor candidate set, and a feature-rich learning-to-rank
model to select distractors that are both plausible and reliable. Experimental
results on datasets across four domains show that our framework yields
distractors that are more plausible and reliable than previous methods. This
dataset can also be used as a benchmark for distractor generation in the
future.
