Papers by Peter Keegan
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting (2022.findings-naacl)
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Jesin James, Vithya Yogarajan, Isabella Shields, Catherine Watson, Peter Keegan, Keoni Mahelona, Peter-Lucas Jones
| Challenge: | Te reo Mori is New Zealand’s only indigenous language spoken by 4.5% of the population of 5 million. |
| Approach: | They train bilingual sub-word embeddings to detect Mori-English code-switching points using a cloud-based multilingual system such as Google and Microsoft Azure. |
| Outcome: | The proposed model outperforms large-scale contextual models on down streaming tasks of detecting Mori language. |