Part of Speech and Universal Dependency effects on English Arabic Machine Translation
This addresses the problem of fine-tuning neural and machine learning models in machine translation, though it appears incremental as it focuses on evaluation rather than a new translation method.
The paper tackles the challenge of evaluating machine translation models by developing a method to assess their performance on syntactical phenomena between English and Arabic, aiming to facilitate easier and more diverse evaluation to improve these models.
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages. This method is especially important as such "neural" and "machine learning" are hard to fine-tune and change. Thus, finding a way to evaluate them easily and diversely would greatly help the task of bettering them.