Unbabel-IST at the WMT Chat Translation Shared Task
João Alves, Pedro Henrique Martins, José G. C. de Souza, M. Amin Farajian, André F. T. Martins
Correct Metadata for
Abstract
We present the joint contribution of IST and Unbabel to the WMT 2022 Chat Translation Shared Task. We participated in all six language directions (English ↔ German, English ↔ French, English ↔ Brazilian Portuguese). Due to the lack of domain-specific data, we use mBART50, a large pretrained language model trained on millions of sentence-pairs, as our base model. We fine-tune it using a two step fine-tuning process. In the first step, we fine-tune the model on publicly available data. In the second step, we use the validation set. After having a domain specific model, we explore the use of kNN-MT as a way of incorporating domain-specific data at decoding time.- Anthology ID:
- 2022.wmt-1.89
- 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:
- 943–948
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.89/
- DOI:
- 10.18653/v1/2022.wmt-1.89
- Bibkey:
- Cite (ACL):
- João Alves, Pedro Henrique Martins, José G. C. de Souza, M. Amin Farajian, and André F. T. Martins. 2022. Unbabel-IST at the WMT Chat Translation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 943–948, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Unbabel-IST at the WMT Chat Translation Shared Task (Alves et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.89.pdf
Export citation
@inproceedings{alves-etal-2022-unbabel,
title = "Unbabel-{IST} at the {WMT} Chat Translation Shared Task",
author = "Alves, Jo{\~a}o and
Martins, Pedro Henrique and
C. de Souza, Jos{\'e} G. and
Farajian, M. Amin and
Martins, Andr{\'e} F. T.",
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-1.89/",
doi = "10.18653/v1/2022.wmt-1.89",
pages = "943--948",
abstract = "We present the joint contribution of IST and Unbabel to the WMT 2022 Chat Translation Shared Task. We participated in all six language directions (English {\ensuremath{\leftrightarrow}} German, English {\ensuremath{\leftrightarrow}} French, English {\ensuremath{\leftrightarrow}} Brazilian Portuguese). Due to the lack of domain-specific data, we use mBART50, a large pretrained language model trained on millions of sentence-pairs, as our base model. We fine-tune it using a two step fine-tuning process. In the first step, we fine-tune the model on publicly available data. In the second step, we use the validation set. After having a domain specific model, we explore the use of kNN-MT as a way of incorporating domain-specific data at decoding time."
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%0 Conference Proceedings %T Unbabel-IST at the WMT Chat Translation Shared Task %A Alves, João %A Martins, Pedro Henrique %A C. de Souza, José G. %A Farajian, M. Amin %A Martins, André F. T. %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 alves-etal-2022-unbabel %X We present the joint contribution of IST and Unbabel to the WMT 2022 Chat Translation Shared Task. We participated in all six language directions (English \ensuremathłeftrightarrow German, English \ensuremathłeftrightarrow French, English \ensuremathłeftrightarrow Brazilian Portuguese). Due to the lack of domain-specific data, we use mBART50, a large pretrained language model trained on millions of sentence-pairs, as our base model. We fine-tune it using a two step fine-tuning process. In the first step, we fine-tune the model on publicly available data. In the second step, we use the validation set. After having a domain specific model, we explore the use of kNN-MT as a way of incorporating domain-specific data at decoding time. %R 10.18653/v1/2022.wmt-1.89 %U https://aclanthology.org/2022.wmt-1.89/ %U https://doi.org/10.18653/v1/2022.wmt-1.89 %P 943-948
Markdown (Informal)
[Unbabel-IST at the WMT Chat Translation Shared Task](https://aclanthology.org/2022.wmt-1.89/) (Alves et al., WMT 2022)
- Unbabel-IST at the WMT Chat Translation Shared Task (Alves et al., WMT 2022)
ACL
- João Alves, Pedro Henrique Martins, José G. C. de Souza, M. Amin Farajian, and André F. T. Martins. 2022. Unbabel-IST at the WMT Chat Translation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 943–948, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.