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.