In this paper, we propose a method for incorporating world knowledge (linked entities and fine-grained entity types) into a neural question generation model. This world knowledge helps to encode additional information related to the entities present in the passage required to generate human-like questions. We evaluate our models on both SQuAD and MS MARCO to demonstrate the usefulness of the world knowledge features. The proposed world knowledge enriched question generation model is able to outperform the vanilla neural question generation model by 1.37 and 1.59 absolute BLEU 4 score on SQuAD and MS MARCO test dataset respectively.

翻译：本文提出一种将世界知识（关联实体与细粒度实体类型）融入神经问题生成模型的方法。该世界知识有助于编码生成类人问题所需文本中实体相关的附加信息。我们在SQuAD和MS MARCO数据集上评估模型，验证了世界知识特征的有效性。所提出的世界知识增强型问题生成模型在SQuAD和MS MARCO测试集上的绝对BLEU-4分数分别比基准神经问题生成模型提升1.37和1.59。