  Current state-of-the-art systems for sequence labeling are typically based on
the family of Recurrent Neural Networks (RNNs). However, the shallow
connections between consecutive hidden states of RNNs and insufficient modeling
of global information restrict the potential performance of those models. In
this paper, we try to address these issues, and thus propose a Global Context
enhanced Deep Transition architecture for sequence labeling named GCDT. We
deepen the state transition path at each position in a sentence, and further
assign every token with a global representation learned from the entire
sentence. Experiments on two standard sequence labeling tasks show that, given
only training data and the ubiquitous word embeddings (Glove), our GCDT
achieves 91.96 F1 on the CoNLL03 NER task and 95.43 F1 on the CoNLL2000
Chunking task, which outperforms the best reported results under the same
settings. Furthermore, by leveraging BERT as an additional resource, we
establish new state-of-the-art results with 93.47 F1 on NER and 97.30 F1 on
Chunking.
