Neural Question Generation with Transfer Learning and Utilization of External Knowledge

Neural question generation (NQG) applies deep neural networks to solve the problem of automatically generating questions from text documents. The performance of deep neural networks relies heavily on the availability of a large amount of labelled training data. For domains where labelled training data are very limited, NQG models suffers from poor performance. Another problem that NQG encounters is the problem of rare and unknown words that occur during training and inference but do not exist in the vocabulary list. We first investigate the impact of transfer learning on NQG, and explore the effects of transferring knowledge learned from data in a general domain into different layers of the NQG network. To deal with the rare and unseen word problem, we integrate semantic relationships in the WordNet lexical database, which is a type of general knowledge external to the training data, into the input representation of the NQG system