Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning

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.

Authors

• Bing Tian
• Yong Zhang
• Jin Wang
• Chunxiao Xing

Keywords

• Machine Learning: Transfer, Adaptation, Multi-task Learning
• Natural Language Processing: Sentiment Analysis and Text Mining
• Natural Language Processing: Text Classification