arxiv:2412.02056

A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala

Published on Dec 3, 2024

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Abstract

A multilingual Named Entity Recognition dataset for low-resource languages is introduced along with benchmark results using multilingual language models and demonstration of its effectiveness in neural machine translation.

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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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