In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions. The incorporates general-purpose knowledge base effectively create small distractor candidate set, and feature-rich learning-to-rank model select distractors that are both plausible reliable. Experimental results on new dataset across four...

Automated question quality rating (AQQR) aims to evaluate through computational means, thereby addressing emerging challenges in online learnersourced repositories. Existing methods for AQQR rely solely on explicitly-defined criteria such as readability and word count, while not fully utilising the power of state-of-the-art deep-learning techniques. We propose DeepQR, a novel neural-network mod...