A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Generative Multi-task Learning for Text Classification
2020
IEEE Access
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 multilabel classification decoder and a hierarchical categorization decoder. In the two decoders, a label-orderindependent multi-label classification loss function and a hierarchical structure mask matrix are
doi:10.1109/access.2020.2991337
fatcat:bdfsl7hrhjgntomapzezuy4hye