AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and Nobiin
This addresses the problem of limited NLP resources for African languages, which represent over 31% of spoken languages, though it is incremental as it applies existing methods to new data.
The paper tackles the lack of word embedding models for African languages by building Word2Vec and Poincaré models for Fon and Nobiin, showing promising results and testing transfer learning to mitigate resource scarcity.
From Word2Vec to GloVe, word embedding models have played key roles in the current state-of-the-art results achieved in Natural Language Processing. Designed to give significant and unique vectorized representations of words and entities, those models have proven to efficiently extract similarities and establish relationships reflecting semantic and contextual meaning among words and entities. African Languages, representing more than 31% of the worldwide spoken languages, have recently been subject to lots of research. However, to the best of our knowledge, there are currently very few to none word embedding models for those languages words and entities, and none for the languages under study in this paper. After describing Glove, Word2Vec, and Poincaré embeddings functionalities, we build Word2Vec and Poincaré word embedding models for Fon and Nobiin, which show promising results. We test the applicability of transfer learning between these models as a landmark for African Languages to jointly involve in mitigating the scarcity of their resources, and attempt to provide linguistic and social interpretations of our results. Our main contribution is to arouse more interest in creating word embedding models proper to African Languages, ready for use, and that can significantly improve the performances of Natural Language Processing downstream tasks on them. The official repository and implementation is at https://github.com/bonaventuredossou/afrivec