Hierarchical Inter-Attention Network for
Document Classification with Multi-Task Learning
Abstract
Document classification is an essential task in many
real world applications. Existing approaches adopt
both text semantics and document structure to obtain the document representation. However, these
models usually require a large collection of annotated training instances, which are not always feasible, especially in low-resource settings. In this
paper, we propose a multi-task learning framework
to jointly train multiple related document classification tasks. We devise a hierarchical architecture to
make use of the shared knowledge from all tasks to
enhance the document representation of each task.
We further propose an inter-attention approach to
improve the task-specific modeling of documents
with global information. Experimental results on
15 public datasets demonstrate the benefits of our
proposed model