The Challenge of Diacritics in Yoruba Embeddings

NeurIPS 2020

Abstract: The major contributions of this work include the empirical establishment of a better performance for Yoruba embeddings from undiacritized (normalized) dataset and provision of new analogy sets for evaluation. The Yoruba language, being a tonal language, utilizes diacritics (tonal marks) in written form. We show that this affects embedding performance by creating embeddings from exactly the same Wikipedia dataset but with the second one normalized to be undiacritized. We further compare average intrinsic performance with two other work (using analogy test set & WordSim) and we obtain the best performance in WordSim and corresponding Spearman correlation. Authors: Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki (Luleå University of Technology)