Akan-English: Transformer for Low Resource Translation

Abstract

Recent years, Neural Machine Translation outperforms the existing traditional-based models for language translation with the use of sequential architecture. However, low resource languages, such as Akan-Twi, lack structured language resources largely due to the limited amount of research geared towards their revival and resuscitation. In an effort to mitigate the issue of low resource language translation from Akan-Twi to English in machine learning, we utilize the low resource parallel corpus for the translation setting of Akan-English. Using our proposed training process and Transformer-based model, we generate augmented corpus which is used to improve the performance of our final translation model. We achieved best BLEU score of 12.96 and 17.57 for the Akan-English and English-Akan, respectively.