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Named Entity Recognition (NER) is a fundamental task in the fields of naturallanguage processing and information extraction. NER has been widely used as astandalone tool or an essential component in a variety of applications such asquestion answering, dialogue assistants and knowledge graphs development.However, training reliable NER models requires a large amount of labelled datawhich is expensive to obtain, particularly in specialized domains. This paperdescribes a method to learn a domain-specific NER model for an arbitrary set ofnamed entities when domain-specific supervision is not available. We assumethat the supervision can be obtained with no human effort, and neural modelscan learn from each other. The code, data and models are publicly available.

Domain-Transferable Method for Named Entity Recognition Task