Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions
This addresses the need for efficient distractor generation in educational assessments, though it is incremental as it builds on existing methods with a configurable approach.
The paper tackled the problem of automatically generating plausible and reliable distractors for open-domain cloze-style multiple-choice questions, resulting in a framework that outperformed previous methods across four domains.
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.