Abstract
In this paper, we describe our systems submitted to the very low resource supervised translation task at WMT20. We participate in both translation directions for Upper Sorbian-German language pair. Our primary submission is a subword-level Transformer-based neural machine translation model trained on original training bitext. We also conduct several experiments with backtranslation using limited monolingual data in our post-submission work and include our results for the same. In one such experiment, we observe jumps of up to 2.6 BLEU points over the primary system by pretraining on a synthetic, backtranslated corpus followed by fine-tuning on the original parallel training data.- Anthology ID:
- 2020.wmt-1.136
- 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:
- 1144–1149
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.136
- DOI:
- Bibkey:
- Cite (ACL):
- Keshaw Singh. 2020. Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20. In Proceedings of the Fifth Conference on Machine Translation, pages 1144–1149, Online. Association for Computational Linguistics.
- Cite (Informal):
- Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20 (Singh, WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.136.pdf
- Video:
- https://slideslive.com/38939621
Export citation
@inproceedings{singh-2020-adobe,
title = "Adobe {AMPS}{'}s Submission for Very Low Resource Supervised Translation Task at {WMT}20",
author = "Singh, Keshaw",
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-1.136",
pages = "1144--1149",
abstract = "In this paper, we describe our systems submitted to the very low resource supervised translation task at WMT20. We participate in both translation directions for Upper Sorbian-German language pair. Our primary submission is a subword-level Transformer-based neural machine translation model trained on original training bitext. We also conduct several experiments with backtranslation using limited monolingual data in our post-submission work and include our results for the same. In one such experiment, we observe jumps of up to 2.6 BLEU points over the primary system by pretraining on a synthetic, backtranslated corpus followed by fine-tuning on the original parallel training data.",
}
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<abstract>In this paper, we describe our systems submitted to the very low resource supervised translation task at WMT20. We participate in both translation directions for Upper Sorbian-German language pair. Our primary submission is a subword-level Transformer-based neural machine translation model trained on original training bitext. We also conduct several experiments with backtranslation using limited monolingual data in our post-submission work and include our results for the same. In one such experiment, we observe jumps of up to 2.6 BLEU points over the primary system by pretraining on a synthetic, backtranslated corpus followed by fine-tuning on the original parallel training data.</abstract>
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%0 Conference Proceedings %T Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20 %A Singh, Keshaw %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-2020-adobe %X In this paper, we describe our systems submitted to the very low resource supervised translation task at WMT20. We participate in both translation directions for Upper Sorbian-German language pair. Our primary submission is a subword-level Transformer-based neural machine translation model trained on original training bitext. We also conduct several experiments with backtranslation using limited monolingual data in our post-submission work and include our results for the same. In one such experiment, we observe jumps of up to 2.6 BLEU points over the primary system by pretraining on a synthetic, backtranslated corpus followed by fine-tuning on the original parallel training data. %U https://aclanthology.org/2020.wmt-1.136 %P 1144-1149
Markdown (Informal)
[Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20](https://aclanthology.org/2020.wmt-1.136) (Singh, WMT 2020)
- Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20 (Singh, WMT 2020)
ACL
- Keshaw Singh. 2020. Adobe AMPS’s Submission for Very Low Resource Supervised Translation Task at WMT20. In Proceedings of the Fifth Conference on Machine Translation, pages 1144–1149, Online. Association for Computational Linguistics.