An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario

Benyamin Ahmadnia, Raul Aranovich


Abstract
In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms. Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality compared to the English-Persian Statistical Machine Translation (SMT) paradigm.
Anthology ID:
2020.wat-1.2
Volume:
Proceedings of the 7th Workshop on Asian Translation
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Toshiaki Nakazawa, Hideki Nakayama, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Win Pa Pa, Ondřej Bojar, Shantipriya Parida, Isao Goto, Hidaya Mino, Hiroshi Manabe, Katsuhito Sudoh, Sadao Kurohashi, Pushpak Bhattacharyya
Venue:
WAT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–49
Language:
URL:
https://aclanthology.org/2020.wat-1.2
DOI:
Bibkey:
Cite (ACL):
Benyamin Ahmadnia and Raul Aranovich. 2020. An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario. In Proceedings of the 7th Workshop on Asian Translation, pages 45–49, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario (Ahmadnia & Aranovich, WAT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wat-1.2.pdf