🤖 AI Summary
Neural question generation suffers from weak entity semantic representation and produces questions with limited human readability and semantic plausibility. Method: This paper introduces structured world knowledge—specifically Wikidata-linked entities and their fine-grained types—into an encoder-decoder framework for the first time. It jointly models entity linking, type-aware embeddings, and hierarchical attention to enhance semantic understanding of key passage entities and improve controlled question generation. The model is trained end-to-end on SQuAD and MS MARCO. Results: Experiments show substantial improvements in generation quality, with absolute BLEU-4 gains of +1.37 and +1.59 over strong baselines. The core contribution is a novel, interpretable, and scalable paradigm for injecting external world knowledge, advancing semantic fidelity and linguistic naturalness in question generation.
📝 Abstract
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.
