Document Level NMT of Low-Resource Languages with Backtranslation
Sami Ul Haq, Sadaf Abdul Rauf, Arsalan Shaukat, Abdullah Saeed
Abstract
This paper describes our system submission to WMT20 shared task on similar language translation. We examined the use of documentlevel neural machine translation (NMT) systems for low-resource, similar language pair Marathi−Hindi. Our system is an extension of state-of-the-art Transformer architecture with hierarchical attention networks to incorporate contextual information. Since, NMT requires large amount of parallel data which is not available for this task, our approach is focused on utilizing monolingual data with back translation to train our models. Our experiments reveal that document-level NMT can be a reasonable alternative to sentence-level NMT for improving translation quality of low resourced languages even when used with synthetic data.- Anthology ID:
- 2020.wmt-1.53
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 442–446
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.53
- DOI:
- Bibkey:
- Cite (ACL):
- Sami Ul Haq, Sadaf Abdul Rauf, Arsalan Shaukat, and Abdullah Saeed. 2020. Document Level NMT of Low-Resource Languages with Backtranslation. In Proceedings of the Fifth Conference on Machine Translation, pages 442–446, Online. Association for Computational Linguistics.
- Cite (Informal):
- Document Level NMT of Low-Resource Languages with Backtranslation (Ul Haq et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.53.pdf
- Video:
- https://slideslive.com/38939608
Export citation
@inproceedings{ul-haq-etal-2020-document,
title = "Document Level {NMT} of Low-Resource Languages with Backtranslation",
author = "Ul Haq, Sami and
Abdul Rauf, Sadaf and
Shaukat, Arsalan and
Saeed, Abdullah",
editor = {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
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.53",
pages = "442--446",
abstract = "This paper describes our system submission to WMT20 shared task on similar language translation. We examined the use of documentlevel neural machine translation (NMT) systems for low-resource, similar language pair Marathi−Hindi. Our system is an extension of state-of-the-art Transformer architecture with hierarchical attention networks to incorporate contextual information. Since, NMT requires large amount of parallel data which is not available for this task, our approach is focused on utilizing monolingual data with back translation to train our models. Our experiments reveal that document-level NMT can be a reasonable alternative to sentence-level NMT for improving translation quality of low resourced languages even when used with synthetic data.",
}
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%0 Conference Proceedings %T Document Level NMT of Low-Resource Languages with Backtranslation %A Ul Haq, Sami %A Abdul Rauf, Sadaf %A Shaukat, Arsalan %A Saeed, Abdullah %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 Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F ul-haq-etal-2020-document %X This paper describes our system submission to WMT20 shared task on similar language translation. We examined the use of documentlevel neural machine translation (NMT) systems for low-resource, similar language pair Marathi−Hindi. Our system is an extension of state-of-the-art Transformer architecture with hierarchical attention networks to incorporate contextual information. Since, NMT requires large amount of parallel data which is not available for this task, our approach is focused on utilizing monolingual data with back translation to train our models. Our experiments reveal that document-level NMT can be a reasonable alternative to sentence-level NMT for improving translation quality of low resourced languages even when used with synthetic data. %U https://aclanthology.org/2020.wmt-1.53 %P 442-446
Markdown (Informal)
[Document Level NMT of Low-Resource Languages with Backtranslation](https://aclanthology.org/2020.wmt-1.53) (Ul Haq et al., WMT 2020)
- Document Level NMT of Low-Resource Languages with Backtranslation (Ul Haq et al., WMT 2020)
ACL
- Sami Ul Haq, Sadaf Abdul Rauf, Arsalan Shaukat, and Abdullah Saeed. 2020. Document Level NMT of Low-Resource Languages with Backtranslation. In Proceedings of the Fifth Conference on Machine Translation, pages 442–446, Online. Association for Computational Linguistics.