The first neural machine translation system for the Erzya language

David Dale


Abstract
We present the first neural machine translation system for translation between the endangered Erzya language and Russian and the dataset collected by us to train and evaluate it. The BLEU scores are 17 and 19 for translation to Erzya and Russian respectively, and more than half of the translations are rated as acceptable by native speakers. We also adapt our model to translate between Erzya and 10 other languages, but without additional parallel data, the quality on these directions remains low. We release the translation models along with the collected text corpus, a new language identification model, and a multilingual sentence encoder adapted for the Erzya language. These resources will be available at https://github.com/slone-nlp/myv-nmt.
Anthology ID:
2022.fieldmatters-1.6
Volume:
Proceedings of the first workshop on NLP applications to field linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Oleg Serikov, Ekaterina Voloshina, Anna Postnikova, Elena Klyachko, Ekaterina Neminova, Ekaterina Vylomova, Tatiana Shavrina, Eric Le Ferrand, Valentin Malykh, Francis Tyers, Timofey Arkhangelskiy, Vladislav Mikhailov, Alena Fenogenova
Venue:
FieldMatters
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
45–53
Language:
URL:
https://aclanthology.org/2022.fieldmatters-1.6
DOI:
Bibkey:
Cite (ACL):
David Dale. 2022. The first neural machine translation system for the Erzya language. In Proceedings of the first workshop on NLP applications to field linguistics, pages 45–53, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
The first neural machine translation system for the Erzya language (Dale, FieldMatters 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.fieldmatters-1.6.pdf
Code
 slone-nlp/myv-nmt
Data
CCMatrix