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.

Full paper


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