Semi-supervised sequence tagging with bidirectional language models
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.
Also cited · not yet reviewed (1)
- Limits of Language Modeling2016 · cited 5×, 1 in Method“Recent state of the art neural language models (Józefowicz et al. 2016) use a similar architecture to our baseline sequence tagger where they pass a token representation (either from a CNN over characters or as token emb…”From this paper · §Language model augmented sequence taggers (TagLM)
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- ELMo2018 · cited 9×, 3 in Method“Overall, this formulation is similar to the approach of Peters et al. 2017, with the exception that we share some weights between directions instead of using completely independent parameters.”From ELMo · §ELMo: Embeddings from Language Models
Abstract
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. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.