Massive vs. Curated Embeddings for Low-Resourced Languages:the Case of Yorùbá and Twi
Table 4: CWE embeddings: Spearman ρ correlation between human evaluation and embedding similarities for the three datasets analysed (C1, C2 and C3).
['[BOLD] Model', 'Twi  [BOLD] Vocab Size', 'Twi  [BOLD] Spearman  [ITALIC] ρ', 'Yorùbá  [BOLD] Vocab Size', 'Yorùbá  [BOLD] Spearman  [ITALIC] ρ']
If we compare the relative numbers for the three datasets (C1, C2 and C3) we observe the same trends as before: the performance of the embeddings in the similarity task improves with the vocabulary size when the training data can be considered clean, but the performance diminishes when the data is noisy.