Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages
Rahul Tangsali, Aditya Vyawahare, Aditya Mandke, Onkar Litake, Dipali Kadam
Abstract
This paper presents the work of team PICT-NLP for the shared task on unsupervised and very low-resource supervised machine translation, organized by the Workshop on Machine Translation, a workshop in collocation with the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). The paper delineates the approaches we implemented for supervised and unsupervised translation between the following 6 language pairs: German-Lower Sorbian (de-dsb), Lower Sorbian-German (dsb-de), Lower Sorbian-Upper Sorbian (dsb-hsb), Upper Sorbian-Lower Sorbian (hsb-dsb), German-Upper Sorbian (de-hsb), and Upper Sorbian-German (hsb-de). For supervised learning, we implemented the transformer architecture from scratch using the Fairseq library. Whereas for unsupervised learning, we implemented Facebook’s XLM masked language modeling approach. We discuss the training details for the models we used, and the results obtained from our approaches. We used the BLEU and chrF metrics for evaluating the accuracies of the generated translations on our systems.- Anthology ID:
- 2022.wmt-1.108
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1104–1110
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.108
- DOI:
- Bibkey:
- Cite (ACL):
- Rahul Tangsali, Aditya Vyawahare, Aditya Mandke, Onkar Litake, and Dipali Kadam. 2022. Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1104–1110, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages (Tangsali et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.108.pdf
Export citation
@inproceedings{tangsali-etal-2022-unsupervised,
title = "Unsupervised and Very-Low Resource Supervised Translation on {G}erman and Sorbian Variant Languages",
author = "Tangsali, Rahul and
Vyawahare, Aditya and
Mandke, Aditya and
Litake, Onkar and
Kadam, Dipali",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.108",
pages = "1104--1110",
abstract = "This paper presents the work of team PICT-NLP for the shared task on unsupervised and very low-resource supervised machine translation, organized by the Workshop on Machine Translation, a workshop in collocation with the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). The paper delineates the approaches we implemented for supervised and unsupervised translation between the following 6 language pairs: German-Lower Sorbian (de-dsb), Lower Sorbian-German (dsb-de), Lower Sorbian-Upper Sorbian (dsb-hsb), Upper Sorbian-Lower Sorbian (hsb-dsb), German-Upper Sorbian (de-hsb), and Upper Sorbian-German (hsb-de). For supervised learning, we implemented the transformer architecture from scratch using the Fairseq library. Whereas for unsupervised learning, we implemented Facebook{'}s XLM masked language modeling approach. We discuss the training details for the models we used, and the results obtained from our approaches. We used the BLEU and chrF metrics for evaluating the accuracies of the generated translations on our systems.",
}
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%0 Conference Proceedings %T Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages %A Tangsali, Rahul %A Vyawahare, Aditya %A Mandke, Aditya %A Litake, Onkar %A Kadam, Dipali %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F tangsali-etal-2022-unsupervised %X This paper presents the work of team PICT-NLP for the shared task on unsupervised and very low-resource supervised machine translation, organized by the Workshop on Machine Translation, a workshop in collocation with the Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). The paper delineates the approaches we implemented for supervised and unsupervised translation between the following 6 language pairs: German-Lower Sorbian (de-dsb), Lower Sorbian-German (dsb-de), Lower Sorbian-Upper Sorbian (dsb-hsb), Upper Sorbian-Lower Sorbian (hsb-dsb), German-Upper Sorbian (de-hsb), and Upper Sorbian-German (hsb-de). For supervised learning, we implemented the transformer architecture from scratch using the Fairseq library. Whereas for unsupervised learning, we implemented Facebook’s XLM masked language modeling approach. We discuss the training details for the models we used, and the results obtained from our approaches. We used the BLEU and chrF metrics for evaluating the accuracies of the generated translations on our systems. %U https://aclanthology.org/2022.wmt-1.108 %P 1104-1110
Markdown (Informal)
[Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages](https://aclanthology.org/2022.wmt-1.108) (Tangsali et al., WMT 2022)
- Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages (Tangsali et al., WMT 2022)
ACL
- Rahul Tangsali, Aditya Vyawahare, Aditya Mandke, Onkar Litake, and Dipali Kadam. 2022. Unsupervised and Very-Low Resource Supervised Translation on German and Sorbian Variant Languages. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1104–1110, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.