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This paper investigates the problem of Named Entity Recognition (NER) forextreme low-resource languages with only a few hundred tagged data samples. NERis a fundamental task in Natural Language Processing (NLP). A critical driveraccelerating NER systems' progress is the existence of large-scale languagecorpora that enable NER systems to achieve outstanding performance in languagessuch as English and French with abundant training data. However, NER forlow-resource languages remains relatively unexplored. In this paper, weintroduce Mask Augmented Named Entity Recognition (MANER), a new methodologythat leverages the distributional hypothesis of pre-trained masked languagemodels (MLMs) for NER. The <mask> token in pre-trained MLMs encodes valuablesemantic contextual information. MANER re-purposes the <mask> token for NERprediction. Specifically, we prepend the <mask> token to every word in asentence for which we would like to predict the named entity tag. Duringtraining, we jointly fine-tune the MLM and a new NER prediction head attachedto each <mask> token. We demonstrate that MANER is well-suited for NER inlow-resource languages; our experiments show that for 100 languages with as fewas 100 training examples, it improves on state-of-the-art methods by up to 48%and by 12% on average on F1 score. We also perform detailed analyses andablation studies to understand the scenarios that are best-suited to MANER.

MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages