构建英泰僧三语并行命名实体标注数据集，提升低资源语言识别性能。
A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
- 构建英泰僧三语并行命名实体标注数据集
- 在低资源语言上实现新基准NER性能
- 适用于多语言NLP与低资源翻译研究
本文提出一个英、泰米尔、僧伽罗三语并行的命名实体标注语料库，其中僧伽罗语和泰米尔语为低资源语言。利用预训练多语言语言模型（mLMs），我们在该数据集上建立了僧伽罗语和泰米尔语的新基准命名实体识别（NER）结果。进一步深入分析了不同mLMs在NER任务上的表现能力。最后，展示了该NER系统在低资源神经机器翻译（NMT）任务中的实际效用。数据集已公开：https://github.com/suralk/multiNER。
原文摘要 · Abstract (English)
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.
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