Yang et al. (2016)[1]:
"We present a deep hierarchical recurrent neural network for sequence tagging. Given a sequence of words, our model employs deep gated recurrent units on both character and word levels to encode morphology and context information, and applies a conditional random field layer to predict the tags. Our model is task independent, language independent, and feature engineering free. We further extend our model to multi-task and crosslingual joint training by sharing the architecture and parameters."
Diagram from Yang et al. (2016)