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arXiv · 1909.03716

Improving Neural Question Generation using World Knowledge

Deepak Gupta·Kaheer Suleman·Mahmoud Adada·Andrew McNamara·Justin Harris
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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.

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Deepak Gupta, Kaheer Suleman, Mahmoud Adada, Andrew McNamara, Justin Harris. 2019-09-09. Improving Neural Question Generation using World Knowledge. https://arxiv.org/abs/1909.03716

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