Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation

Mersad Esalati, Mohammad Javad Dousti, Heshaam Faili


Abstract
Neural machine translation requires large number of parallel sentences along with in-domain parallel data to attain best results. Nevertheless, no scientific parallel corpus for English-Persian language pair is available. In this paper, a parallel corpus called Esposito is introduced, which contains 3.5 million parallel sentences in the scientific domain for English-Persian language pair. In addition, we present a manually validated scientific test set that might serve as a baseline for future studies. We show that a system trained using Esposito along with other publicly available data improves the baseline on average by 7.6 and 8.4 BLEU scores for En->Fa and Fa->En directions, respectively. Additionally, domain analysis using the 5-gram KenLM model revealed notable distinctions between our parallel corpus and the existing generic parallel corpus. This dataset will be available to the public upon the acceptance of the paper.
Anthology ID:
2024.lrec-main.557
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
6299–6308
Language:
URL:
https://aclanthology.org/2024.lrec-main.557
DOI:
Bibkey:
Cite (ACL):
Mersad Esalati, Mohammad Javad Dousti, and Heshaam Faili. 2024. Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6299–6308, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (Esalati et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.557.pdf