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。

运用辅助语言和迁移学习技术提高低资源实体命名识别