This paper presents a multi-way parallel English-Tamil-Sinhala corpus annotated with Named Entities (NEs), where Sinhala and Tamil are low-resource languages. Using pre-trained multilingual Language Models (mLMs), we establish new benchmark Named Entity Recognition (NER) results on this dataset for Sinhala and Tamil. We also carry out a detailed investigation on the NER capabilities of different types of mLMs. Finally, we demonstrate the utility of our NER system on a low-resource Neural Machine Translation (NMT) task. Our dataset is publicly released: https://github.com/suralk/multiNER.

翻译：本文提出了一个多向平行的英语-泰米尔语-僧伽罗语命名实体标注语料库，其中僧伽罗语和泰米尔语属于低资源语言。利用预训练的多语言语言模型，我们在此数据集上为僧伽罗语和泰米尔语建立了新的命名实体识别基准结果。我们还对不同类型多语言语言模型的命名实体识别能力进行了详细研究。最后，我们在一个低资源神经机器翻译任务上展示了我们命名实体识别系统的实用性。我们的数据集已公开发布：https://github.com/suralk/multiNER。