Multi-Task Cross-Lingual Sequence Tagging from Scratch

Understand

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 cross-lingual joint training by sharing the architecture and parameters.