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
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 45–49
- Language:
- URL:
- https://aclanthology.org/2020.wat-1.2
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.wat-1.2.pdf