Interpretable answer retrieval based on heterogeneous network embedding

Yongliang Wu, Xiao Pan, Jinghui Li, Shimao Dou, Xiaoxue Wang

Abstract: Highlight•Multi-entity fusion and low interpretability of answers are urgent issues•We employ heterogeneous graphs for entity fusion of community question answering•The heterogeneous graph neural network is used for entity relation embedding•Meta-paths represent user intention and are pioneered for answer interpretability•Experiments on real datasets verify the interpretable question-answering performance