Multi Task Cross Lingual Sequence Tagging from Scratch
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. Our model achieves state of the art results in multiple languages on several benchmark tasks including POS tagging, chunking, and NER. We also demonstrate that multi task and cross lingual joint training can improve the performance in various cases.
ArXiv