Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques
TL;DR
This study develops a transformer-based machine translation model for Amharic-to-English, addressing data scarcity and complex morphology with character embedding and combined regularization techniques to reduce overfitting.
Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques
Surafiel Habib Asefa; Yaregal Assabie
https://doi.org/10.1109/ACCESS.2024.3521985
Volume 13
Amharic is the working language of Ethiopia and, owing to its Semitic characteristics, the language is known for its complex morphology. It is also an under-resourced language, presenting significant challenges for natural language processing tasks like machine translation. The primary challenges include the scarcity of parallel data, which increases the risk of overfitting and limits the model’s ...