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Linguistic sequence labeling is a general modeling approach that encompassesa variety of problems, such as part-of-speech tagging and named entityrecognition. Recent advances in neural networks (NNs) make it possible to buildreliable models without handcrafted features. However, in many cases, it ishard to obtain sufficient annotations to train these models. In this study, wedevelop a novel neural framework to extract abundant knowledge hidden in rawtexts to empower the sequence labeling task. Besides word-level knowledgecontained in pre-trained word embeddings, character-aware neural languagemodels are incorporated to extract character-level knowledge. Transfer learningtechniques are further adopted to mediate different components and guide thelanguage model towards the key knowledge. Comparing to previous methods, thesetask-specific knowledge allows us to adopt a more concise model and conductmore efficient training. Different from most transfer learning methods, theproposed framework does not rely on any additional supervision. It extractsknowledge from self-contained order information of training sequences.Extensive experiments on benchmark datasets demonstrate the effectiveness ofleveraging character-level knowledge and the efficiency of co-training. Forexample, on the CoNLL03 NER task, model training completes in about 6 hours ona single GPU, reaching F1 score of 91.71$\pm$0.10 without using any extraannotation.

Empower Sequence Labeling with Task-Aware Neural Language Model