Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation
Artur Nowakowski, Gabriela Pałka, Kamil Guttmann, Mikołaj Pokrywka
Abstract
This paper presents Adam Mickiewicz University’s (AMU) submissions to the constrained track of the WMT 2022 General MT Task. We participated in the Ukrainian ↔ Czech translation directions. The systems are a weighted ensemble of four models based on the Transformer (big) architecture. The models use source factors to utilize the information about named entities present in the input. Each of the models in the ensemble was trained using only the data provided by the shared task organizers. A noisy back-translation technique was used to augment the training corpora. One of the models in the ensemble is a document-level model, trained on parallel and synthetic longer sequences. During the sentence-level decoding process, the ensemble generated the n-best list. The n-best list was merged with the n-best list generated by a single document-level model which translated multiple sentences at a time. Finally, existing quality estimation models and minimum Bayes risk decoding were used to rerank the n-best list so that the best hypothesis was chosen according to the COMET evaluation metric. According to the automatic evaluation results, our systems rank first in both translation directions.- Anthology ID:
- 2022.wmt-1.26
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 326–334
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.26
- DOI:
- Bibkey:
- Cite (ACL):
- Artur Nowakowski, Gabriela Pałka, Kamil Guttmann, and Mikołaj Pokrywka. 2022. Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 326–334, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation (Nowakowski et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.26.pdf
Export citation
@inproceedings{nowakowski-etal-2022-adam,
title = "{A}dam {M}ickiewicz {U}niversity at {WMT} 2022: {NER}-Assisted and Quality-Aware Neural Machine Translation",
author = "Nowakowski, Artur and
Pa{\l}ka, Gabriela and
Guttmann, Kamil and
Pokrywka, Miko{\l}aj",
editor = {Koehn, Philipp and
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
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.26",
pages = "326--334",
abstract = "This paper presents Adam Mickiewicz University{'}s (AMU) submissions to the constrained track of the WMT 2022 General MT Task. We participated in the Ukrainian ↔ Czech translation directions. The systems are a weighted ensemble of four models based on the Transformer (big) architecture. The models use source factors to utilize the information about named entities present in the input. Each of the models in the ensemble was trained using only the data provided by the shared task organizers. A noisy back-translation technique was used to augment the training corpora. One of the models in the ensemble is a document-level model, trained on parallel and synthetic longer sequences. During the sentence-level decoding process, the ensemble generated the n-best list. The n-best list was merged with the n-best list generated by a single document-level model which translated multiple sentences at a time. Finally, existing quality estimation models and minimum Bayes risk decoding were used to rerank the n-best list so that the best hypothesis was chosen according to the COMET evaluation metric. According to the automatic evaluation results, our systems rank first in both translation directions.",
}
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%0 Conference Proceedings %T Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation %A Nowakowski, Artur %A Pałka, Gabriela %A Guttmann, Kamil %A Pokrywka, Mikołaj %Y Koehn, Philipp %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 Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F nowakowski-etal-2022-adam %X This paper presents Adam Mickiewicz University’s (AMU) submissions to the constrained track of the WMT 2022 General MT Task. We participated in the Ukrainian ↔ Czech translation directions. The systems are a weighted ensemble of four models based on the Transformer (big) architecture. The models use source factors to utilize the information about named entities present in the input. Each of the models in the ensemble was trained using only the data provided by the shared task organizers. A noisy back-translation technique was used to augment the training corpora. One of the models in the ensemble is a document-level model, trained on parallel and synthetic longer sequences. During the sentence-level decoding process, the ensemble generated the n-best list. The n-best list was merged with the n-best list generated by a single document-level model which translated multiple sentences at a time. Finally, existing quality estimation models and minimum Bayes risk decoding were used to rerank the n-best list so that the best hypothesis was chosen according to the COMET evaluation metric. According to the automatic evaluation results, our systems rank first in both translation directions. %U https://aclanthology.org/2022.wmt-1.26 %P 326-334
Markdown (Informal)
[Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation](https://aclanthology.org/2022.wmt-1.26) (Nowakowski et al., WMT 2022)
- Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation (Nowakowski et al., WMT 2022)
ACL
- Artur Nowakowski, Gabriela Pałka, Kamil Guttmann, and Mikołaj Pokrywka. 2022. Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 326–334, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.