Hunayn: Elevating Translation Beyond the Literal
This work addresses the need for more culturally sensitive and contextually accurate English-to-Arabic translation tools, though it appears incremental as it builds on an existing transformer model with a new dataset.
The researchers tackled the problem of English-to-Arabic translation by fine-tuning the Helsinki transformer on a literary Arabic dataset, resulting in a translator that outperformed Google Translate in qualitative assessments, particularly in cultural sensitivity and context accuracy.
This project introduces an advanced English-to-Arabic translator surpassing conventional tools. Leveraging the Helsinki transformer (MarianMT), our approach involves fine-tuning on a self-scraped, purely literary Arabic dataset. Evaluations against Google Translate show consistent outperformance in qualitative assessments. Notably, it excels in cultural sensitivity and context accuracy. This research underscores the Helsinki transformer's superiority for English-to-Arabic translation using a Fusha dataset.