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

This paper investigates the problem of Named Entity Recognition (NER) for extreme lowresource languages with only a few hundred tagged data samples.A critical enabler of most of the progress in NER is the readily available, large-scale training data for languages such as English and French.However, NER for lowresource languages remains relatively underexplored, leaving much room for improvement.We propose Mask Augmented Named Entity Recognition (MANER), a simple yet effective method that leverages the distributional hypothesis of pre-trained masked language models (MLMs) to improve NER performance for lowresource languages significantly.MANER repurposes the [mask] token in MLMs, which encodes valuable semantic contextual information, for NER prediction.Specifically, we prepend a [mask] token to every word in a sentence and predict the named entity for each word from its preceding [mask] token.We demonstrate that MANER is well-suited for NER in low-resource languages; our experiments show that for 100 languages with as few as 100 training examples, it improves on the state-of-the-art by up to 48% and by 12% on average on F1 score.We also perform detailed analyses and ablation studies to understand the scenarios that are best suited to MANER.