Neural Question Generation with Semantics of Question Type

Abstract

This paper focuses on automatic question generation (QG) that transforms a narrative sentence into an interrogative sentence. Recently, neural networks have been used in this task due to its extraordinary ability of semantics encoding and decoding. We propose an approach which incorporates semantics of the possible question type. We utilize the Convolutional Neural Network (CNN) for predicting question type of the answer phrases in the narrative sentence. In order to incorporate the question type semantics into the generating process, we classify the question type which the answer phrases refer to. In addition, We use Bidirectional Long Short Term Memory (Bi-LSTM) to construct the question generating model. The experiment results show that our method outperforms the baseline system with the improvement of 1.7% on BLEU-4 score and beyonds the state-of-the-art.