Semi-supervised sequence tagging with bidirectional language models

ACL 2017 Matthew E. Peters • Waleed Ammar • Chandra Bhagavatula • Russell Power

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pre- trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Named Entity Recognition CoNLL 2003 (English) Peters et al. F1 91.93 # 7