Standard multilingual models like XLM-RoBERTa-base natively process over 100 languages. However, they often suffer from the "curse of multilinguality," where low-resource languages perform poorly due to insufficient token training data.
Optimizing Multilingual NLP: Leveraging WALS and Universal Dependencies (UD) for RoBERTa Cross-Lingual Transfer wals roberta sets upd
: Define the architecture—often a Transformer-based auto-encoder—and load the specific "WALS" weights or configurations. wals roberta sets upd
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