论文标题
知识驱动的干扰物生成，用于披风风格的多项选择问题
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions
论文作者
论文摘要
在本文中，我们提出了一个新颖的可配置框架，以自动为开放式粘结式悬崖式的多项选择问题生成分心的选择，该问题结合了通用知识基础，以有效地创建一个小型的分散分心的候选者集，以及一个既有特征的学习到零售店的模型又可以选择且可供选择的分散者，这些模型既可以合理又可靠。跨四个领域的数据集的实验结果表明，我们的框架产生的干扰因素比以前的方法更合理和可靠。该数据集在将来也可以用作分散分心的基准。
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.
