TITLE: An Evaluation of Persian-English Machine Translation Datasets with Transformers 

ABSTRACT: Nowadays, many researchers are focusing their attention on the subject of
machine translation (MT). However, Persian machine translation has remained
unexplored despite a vast amount of research being conducted in languages with
high resources, such as English. Moreover, while a substantial amount of
research has been undertaken in statistical machine translation for some
datasets in Persian, there is currently no standard baseline for
transformer-based text2text models on each corpus. This study collected and
analysed the most popular and valuable parallel corpora, which were used for
Persian-English translation. Furthermore, we fine-tuned and evaluated two
state-of-the-art attention-based seq2seq models on each dataset separately (48
results). We hope this paper will assist researchers in comparing their Persian
to English and vice versa machine translation results to a standard baseline.
Nowadays, many researchers are focusing their attention on the subject of
machine translation (MT). However, Persian machine translation has remained
unexplored despite a vast amount of research being conducted in languages with
high resources, such as English. Moreover, while a substantial amount of
research has been undertaken in statistical machine translation for some
datasets in Persian, there is currently no standard baseline for
transformer-based text2text models on each corpus. This study collected and
analysed the most popular and valuable parallel corpora, which were used for
Persian-English translation. Furthermore, we fine-tuned and evaluated two
state-of-the-art attention-based seq2seq models on each dataset separately (48
results). We hope this paper will assist researchers in comparing their Persian
to English and vice versa machine translation results to a standard baseline.
An Evaluation of Persian-English Machine Translation Datasets with Transformers
http://arxiv.org/abs/2302.00321v1