Hierarchical Capsule Based Neural Network Architecture for Sequence Labeling

Sequence Labeling is one of the most prominent tasks in NLP. The traditional text classification models do not carry context from one sentence to another and hence may not perform well on these tasks. These models lack a hierarchical structure that can aid them in dissecting the input structure at different levels to allow flow of context between sentences. In this paper, we propose a hierarchical neural network comprising of Bi-LSTMs, Dilated Convolution operation, Capsules and Conditional Random Field (CRF) to understand the discourse/ abstract structure and predict next probable label by using label history. We have performed experiments on 3 publicly available datasets through which we have demonstrated that our model has achieved state-of-art performance on these datasets.