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CRF has been used as a powerful model for statistical sequence labeling. Forneural sequence labeling, however, BiLSTM-CRF does not always lead to betterresults compared with BiLSTM-softmax local classification. This can be becausethe simple Markov label transition model of CRF does not give much informationgain over strong neural encoding. For better representing label sequences, weinvestigate a hierarchically-refined label attention network, which explicitlyleverages label embeddings and captures potential long-term label dependency bygiving each word incrementally refined label distributions with hierarchicalattention. Results on POS tagging, NER and CCG supertagging show that theproposed model not only improves the overall tagging accuracy with similarnumber of parameters, but also significantly speeds up the training and testingcompared to BiLSTM-CRF.

Hierarchically-Refined Label Attention Network for Sequence Labeling