The NITS-CNLP System for the Unsupervised MT Task at WMT 2020
Salam Michael Singh, Thoudam Doren Singh, Sivaji Bandyopadhyay
Correct Metadata for
Abstract
We describe NITS-CNLP’s submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i.e, using only the data provided by the organizers. We train our unsupervised model using monolingual data from both the languages by jointly pre-training the encoder and decoder and fine-tune using backtranslation loss. The final model uses the source side (de) monolingual data and the target side (hsb) synthetic data as a pseudo-parallel data to train a pseudo-supervised system which is tuned using the provided development set(dev set).- Anthology ID:
- 2020.wmt-1.135
- 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:
- 1139–1143
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.135/
- DOI:
- 10.18653/v1/2020.wmt-1.135
- Bibkey:
- Cite (ACL):
- Salam Michael Singh, Thoudam Doren Singh, and Sivaji Bandyopadhyay. 2020. The NITS-CNLP System for the Unsupervised MT Task at WMT 2020. In Proceedings of the Fifth Conference on Machine Translation, pages 1139–1143, Online. Association for Computational Linguistics.
- Cite (Informal):
- The NITS-CNLP System for the Unsupervised MT Task at WMT 2020 (Singh et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.135.pdf
- Video:
- https://slideslive.com/38939575
Export citation
@inproceedings{singh-etal-2020-nits,
title = "The {NITS}-{CNLP} System for the Unsupervised {MT} Task at {WMT} 2020",
author = "Singh, Salam Michael and
Singh, Thoudam Doren and
Bandyopadhyay, Sivaji",
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-1.135/",
doi = "10.18653/v1/2020.wmt-1.135",
pages = "1139--1143",
abstract = "We describe NITS-CNLP{'}s submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i.e, using only the data provided by the organizers. We train our unsupervised model using monolingual data from both the languages by jointly pre-training the encoder and decoder and fine-tune using backtranslation loss. The final model uses the source side (de) monolingual data and the target side (hsb) synthetic data as a pseudo-parallel data to train a pseudo-supervised system which is tuned using the provided development set(dev set)."
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%0 Conference Proceedings %T The NITS-CNLP System for the Unsupervised MT Task at WMT 2020 %A Singh, Salam Michael %A Singh, Thoudam Doren %A Bandyopadhyay, Sivaji %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 singh-etal-2020-nits %X We describe NITS-CNLP’s submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i.e, using only the data provided by the organizers. We train our unsupervised model using monolingual data from both the languages by jointly pre-training the encoder and decoder and fine-tune using backtranslation loss. The final model uses the source side (de) monolingual data and the target side (hsb) synthetic data as a pseudo-parallel data to train a pseudo-supervised system which is tuned using the provided development set(dev set). %R 10.18653/v1/2020.wmt-1.135 %U https://aclanthology.org/2020.wmt-1.135/ %U https://doi.org/10.18653/v1/2020.wmt-1.135 %P 1139-1143
Markdown (Informal)
[The NITS-CNLP System for the Unsupervised MT Task at WMT 2020](https://aclanthology.org/2020.wmt-1.135/) (Singh et al., WMT 2020)
- The NITS-CNLP System for the Unsupervised MT Task at WMT 2020 (Singh et al., WMT 2020)
ACL
- Salam Michael Singh, Thoudam Doren Singh, and Sivaji Bandyopadhyay. 2020. The NITS-CNLP System for the Unsupervised MT Task at WMT 2020. In Proceedings of the Fifth Conference on Machine Translation, pages 1139–1143, Online. Association for Computational Linguistics.