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Current state-of-the-art systems for sequence labeling are typically based onthe family of Recurrent Neural Networks (RNNs). However, the shallowconnections between consecutive hidden states of RNNs and insufficient modelingof global information restrict the potential performance of those models. Inthis paper, we try to address these issues, and thus propose a Global Contextenhanced Deep Transition architecture for sequence labeling named GCDT. Wedeepen the state transition path at each position in a sentence, and furtherassign every token with a global representation learned from the entiresentence. Experiments on two standard sequence labeling tasks show that, givenonly training data and the ubiquitous word embeddings (Glove), our GCDTachieves 91.96 F1 on the CoNLL03 NER task and 95.43 F1 on the CoNLL2000Chunking task, which outperforms the best reported results under the samesettings. Furthermore, by leveraging BERT as an additional resource, weestablish new state-of-the-art results with 93.47 F1 on NER and 97.30 F1 onChunking.

GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling