@inproceedings{mirzakhalov-etal-2021-large,
title = "A Large-Scale Study of Machine Translation in {T}urkic Languages",
author = "Mirzakhalov, Jamshidbek and
Babu, Anoop and
Ataman, Duygu and
Kariev, Sherzod and
Tyers, Francis and
Abduraufov, Otabek and
Hajili, Mammad and
Ivanova, Sardana and
Khaytbaev, Abror and
Laverghetta Jr., Antonio and
Moydinboyev, Bekhzodbek and
Onal, Esra and
Pulatova, Shaxnoza and
Wahab, Ahsan and
Firat, Orhan and
Chellappan, Sriram",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.475",
doi = "10.18653/v1/2021.emnlp-main.475",
pages = "5876--5890",
abstract = "Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.",
}
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%0 Conference Proceedings
%T A Large-Scale Study of Machine Translation in Turkic Languages
%A Mirzakhalov, Jamshidbek
%A Babu, Anoop
%A Ataman, Duygu
%A Kariev, Sherzod
%A Tyers, Francis
%A Abduraufov, Otabek
%A Hajili, Mammad
%A Ivanova, Sardana
%A Khaytbaev, Abror
%A Laverghetta Jr., Antonio
%A Moydinboyev, Bekhzodbek
%A Onal, Esra
%A Pulatova, Shaxnoza
%A Wahab, Ahsan
%A Firat, Orhan
%A Chellappan, Sriram
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F mirzakhalov-etal-2021-large
%X Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.
%R 10.18653/v1/2021.emnlp-main.475
%U https://aclanthology.org/2021.emnlp-main.475
%U https://doi.org/10.18653/v1/2021.emnlp-main.475
%P 5876-5890
Markdown (Informal)
[A Large-Scale Study of Machine Translation in Turkic Languages](https://aclanthology.org/2021.emnlp-main.475) (Mirzakhalov et al., EMNLP 2021)
ACL
- Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Bekhzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, and Sriram Chellappan. 2021. A Large-Scale Study of Machine Translation in Turkic Languages. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5876–5890, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.