A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 67 references that are in the map. First posted on arXiv 3 Dec 2024.
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References · 25
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