Object-oriented Semantic Graph Based Natural Question Generation

Abstract

Generating a natural question can enable autonomous robots to propose problems according to their surroundings. However, recent studies on question generation rarely consider semantic graph mapping, which is widely used to understand environments. In this paper, we introduce a method to generate natural questions using object-oriented semantic graphs. First, a graph convolutional network extracts features from the graph. Then, a recurrent neural network generates the natural question from the extracted features. Using graphs, we can generate natural questions for both single and sequential scenes. The proposed method outperforms conventional methods on a publicly available dataset for single scenes and can generate questions for sequential scenes.