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Abstract
This report describes YerevaNN’s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task. We provide systems for English-Russian and English-German language pairs. For the English-Russian pair, our submissions achieve the best BLEU scores, with en→ru direction outperforming the other systems by a significant margin. We explain most of the improvements by our heavy data preprocessing pipeline which attempts to fix poorly aligned sentences in the parallel data.- Anthology ID:
- 2020.wmt-1.88
- 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:
- 820–825
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.88/
- DOI:
- Bibkey:
- Cite (ACL):
- Karen Hambardzumyan, Hovhannes Tamoyan, and Hrant Khachatrian. 2020. YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs. In Proceedings of the Fifth Conference on Machine Translation, pages 820–825, Online. Association for Computational Linguistics.
- Cite (Informal):
- YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs (Hambardzumyan et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.88.pdf
- Optionalsupplementarymaterial:
- 2020.wmt-1.88.OptionalSupplementaryMaterial.tgz
- Video:
- https://slideslive.com/38939644
Export citation
@inproceedings{hambardzumyan-etal-2020-yerevanns,
title = "{Y}ereva{NN}`s Systems for {WMT}20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs",
author = "Hambardzumyan, Karen and
Tamoyan, Hovhannes and
Khachatrian, Hrant",
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-1.88/",
pages = "820--825",
abstract = "This report describes YerevaNN`s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task. We provide systems for English-Russian and English-German language pairs. For the English-Russian pair, our submissions achieve the best BLEU scores, with en$\rightarrow$ru direction outperforming the other systems by a significant margin. We explain most of the improvements by our heavy data preprocessing pipeline which attempts to fix poorly aligned sentences in the parallel data."
}
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%0 Conference Proceedings %T YerevaNN‘s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs %A Hambardzumyan, Karen %A Tamoyan, Hovhannes %A Khachatrian, Hrant %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 hambardzumyan-etal-2020-yerevanns %X This report describes YerevaNN‘s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task. We provide systems for English-Russian and English-German language pairs. For the English-Russian pair, our submissions achieve the best BLEU scores, with en\rightarrowru direction outperforming the other systems by a significant margin. We explain most of the improvements by our heavy data preprocessing pipeline which attempts to fix poorly aligned sentences in the parallel data. %U https://aclanthology.org/2020.wmt-1.88/ %P 820-825
Markdown (Informal)
[YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs](https://aclanthology.org/2020.wmt-1.88/) (Hambardzumyan et al., WMT 2020)
- YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs (Hambardzumyan et al., WMT 2020)
ACL
- Karen Hambardzumyan, Hovhannes Tamoyan, and Hrant Khachatrian. 2020. YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs. In Proceedings of the Fifth Conference on Machine Translation, pages 820–825, Online. Association for Computational Linguistics.