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Being able to train Named Entity Recognition (NER) models for emerging topicsis crucial for many real-world applications especially in the medical domainwhere new topics are continuously evolving out of the scope of existing modelsand datasets. For a realistic evaluation setup, we introduce a novel COVID-19news NER dataset (COVIDNEWS-NER) and release 3000 entries of hand annotatedstrongly labelled sentences and 13000 auto-generated weakly labelled sentences.Besides the dataset, we propose CONTROSTER, a recipe to strategically combineweak and strong labels in improving NER in an emerging topic through transferlearning. We show the effectiveness of CONTROSTER on COVIDNEWS-NER whileproviding analysis on combining weak and strong labels for training. Our keyfindings are: (1) Using weak data to formulate an initial backbone beforetuning on strong data outperforms methods trained on only strong or weak data.(2) A combination of out-of-domain and in-domain weak label training is crucialand can overcome saturation when being training on weak labels from a singlesource.

How to tackle an emerging topic? Combining strong and weak labels for Covid news NER