Named Entity Recognition (NER) is a fundamental task in NLP that is used to
locate the key information in text and is primarily applied in conversational
and search systems. In commercial applications, NER or comparable slot-filling
methods have been widely deployed for popular languages. NER is used in
applications such as human resources, customer service, search engines, content
classification, and academia. In this paper, we draw focus on identifying name
entities for low-resource Indian languages that are closely related, like Hindi
and Marathi. We use various adaptations of BERT such as baseBERT, AlBERT, and
RoBERTa to train a supervised NER model. We also compare multilingual models
with monolingual models and establish a baseline. In this work, we show the
assisting capabilities of the Hindi and Marathi languages for the NER task. We
show that models trained using multiple languages perform better than a single
language. However, we also observe that blind mixing of all datasets doesn't
necessarily provide improvements and data selection methods may be required.

论文探究了如何在低资源语言如印地语和马拉地语中通过适应 BERT 模型，训练监督学习的 NER 模型，并比较了多语言模型与单语言模型的性能，研究结果表明多语言模型可以提高 NER 的精度。