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Abstract
In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT’21 Large Scale Multilingual Translation Task - Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task without any training or architecture tricks, relying mainly on the strength of our data preprocessing techniques to boost performance. Our final submission model scored 22.92 average BLEU on the FLORES-101 devtest set, and scored 22.97 average BLEU on the contest’s hidden test set, ranking us sixth overall. Despite using only a standard Transformer, our model ranked first in Indonesian to Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques.- Anthology ID:
- 2021.wmt-1.52
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 431–438
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.52/
- DOI:
- Bibkey:
- Cite (ACL):
- Lintang Sutawika and Jan Christian Blaise Cruz. 2021. Data Processing Matters: SRPH-Konvergen AI’s Machine Translation System for WMT’21. In Proceedings of the Sixth Conference on Machine Translation, pages 431–438, Online. Association for Computational Linguistics.
- Cite (Informal):
- Data Processing Matters: SRPH-Konvergen AI’s Machine Translation System for WMT’21 (Sutawika & Cruz, WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.52.pdf
- Data
- FLoRes-101
Export citation
@inproceedings{sutawika-cruz-2021-data,
title = "Data Processing Matters: {SRPH}-Konvergen {AI}`s Machine Translation System for {WMT}`21",
author = "Sutawika, Lintang and
Cruz, Jan Christian Blaise",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, 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
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.52/",
pages = "431--438",
abstract = "In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT`21 Large Scale Multilingual Translation Task - Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task without any training or architecture tricks, relying mainly on the strength of our data preprocessing techniques to boost performance. Our final submission model scored 22.92 average BLEU on the FLORES-101 devtest set, and scored 22.97 average BLEU on the contest`s hidden test set, ranking us sixth overall. Despite using only a standard Transformer, our model ranked first in Indonesian to Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques."
}
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<abstract>In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT‘21 Large Scale Multilingual Translation Task - Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task without any training or architecture tricks, relying mainly on the strength of our data preprocessing techniques to boost performance. Our final submission model scored 22.92 average BLEU on the FLORES-101 devtest set, and scored 22.97 average BLEU on the contest‘s hidden test set, ranking us sixth overall. Despite using only a standard Transformer, our model ranked first in Indonesian to Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques.</abstract>
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%0 Conference Proceedings %T Data Processing Matters: SRPH-Konvergen AI‘s Machine Translation System for WMT‘21 %A Sutawika, Lintang %A Cruz, Jan Christian Blaise %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, 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 Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F sutawika-cruz-2021-data %X In this paper, we describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT‘21 Large Scale Multilingual Translation Task - Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task without any training or architecture tricks, relying mainly on the strength of our data preprocessing techniques to boost performance. Our final submission model scored 22.92 average BLEU on the FLORES-101 devtest set, and scored 22.97 average BLEU on the contest‘s hidden test set, ranking us sixth overall. Despite using only a standard Transformer, our model ranked first in Indonesian to Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques. %U https://aclanthology.org/2021.wmt-1.52/ %P 431-438
Markdown (Informal)
[Data Processing Matters: SRPH-Konvergen AI’s Machine Translation System for WMT’21](https://aclanthology.org/2021.wmt-1.52/) (Sutawika & Cruz, WMT 2021)
- Data Processing Matters: SRPH-Konvergen AI’s Machine Translation System for WMT’21 (Sutawika & Cruz, WMT 2021)
ACL
- Lintang Sutawika and Jan Christian Blaise Cruz. 2021. Data Processing Matters: SRPH-Konvergen AI’s Machine Translation System for WMT’21. In Proceedings of the Sixth Conference on Machine Translation, pages 431–438, Online. Association for Computational Linguistics.