Papers › Leveraging Context Information for Natural Question Generation

Leveraging Context Information for Natural Question Generation

1 Jun 2018NAACL 2018 6archive 2025-07-28

Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea

The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.

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