Generative Multi-Task Learning for Text Classification
Journal: IEEE Access
Year: 2020

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which is composed of a shared encoder, a multi-label classification decoder and a hierarchical categorization decoder. In the two decoders, a label-order-independent multi-label classification loss function and a hierarchical structure mask matrix are