Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation
Advancing machine translation for low-resource languages using typological embedding initialization
Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon. When fine-tuning these models for an unseen language, practitioners must choose a proxy language token, yet...
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This research provides a more stable methodology for extending multilingual translation models to low-resource languages by removing the guesswork from proxy language selection. By leveraging averaged embeddings, it demonstrates that linguistic typology can effectively bridge the gap for underrepresented languages in global AI systems.
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