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.
@article{DBLP:journals/corr/abs-2106-00745,
author = {Ofek Rafaeli and
Omri Abend and
Leshem Choshen and
Dmitry Nikolaev},
title = {Part of Speech and Universal Dependency effects on English Arabic
Machine Translation},
journal = {CoRR},
volume = {abs/2106.00745},
year = {2021},
url = {https://arxiv.org/abs/2106.00745},
eprinttype = {arXiv},
eprint = {2106.00745},
timestamp = {Mon, 25 Oct 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-00745.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.