Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?
Jesujoba Alabi, Lydia Nishimwe, Benjamin Muller, Camille Rey, Benoît Sagot, Rachel Bawden
Abstract
This paper describes the Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions cs,ru,uk→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results.- Anthology ID:
- 2022.wmt-1.15
- 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:
- 233–243
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.15
- DOI:
- Bibkey:
- Cite (ACL):
- Jesujoba Alabi, Lydia Nishimwe, Benjamin Muller, Camille Rey, Benoît Sagot, and Rachel Bawden. 2022. Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 233–243, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation? (Alabi et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.15.pdf
Export citation
@inproceedings{alabi-etal-2022-inria,
title = "Inria-{ALMA}na{CH} at {WMT} 2022: Does Transcription Help Cross-Script Machine Translation?",
author = "Alabi, Jesujoba and
Nishimwe, Lydia and
Muller, Benjamin and
Rey, Camille and
Sagot, Beno{\^\i}t and
Bawden, Rachel",
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-1.15",
pages = "233--243",
abstract = "This paper describes the Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions cs,ru,uk→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results.",
}
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%0 Conference Proceedings %T Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation? %A Alabi, Jesujoba %A Nishimwe, Lydia %A Muller, Benjamin %A Rey, Camille %A Sagot, Benoît %A Bawden, Rachel %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 alabi-etal-2022-inria %X This paper describes the Inria ALMAnaCH team submission to the WMT 2022 general translation shared task. Participating in the language directions cs,ru,uk→en and cs↔uk, we experiment with the use of a dedicated Latin-script transcription convention aimed at representing all Slavic languages involved in a way that maximises character- and word-level correspondences between them as well as with the English language. Our hypothesis was that bringing the source and target language closer could have a positive impact on machine translation results. We provide multiple comparisons, including bilingual and multilingual baselines, with and without transcription. Initial results indicate that the transcription strategy was not successful, resulting in lower results than baselines. We nevertheless submitted our multilingual, transcribed models as our primary systems, and in this paper provide some indications as to why we got these negative results. %U https://aclanthology.org/2022.wmt-1.15 %P 233-243
Markdown (Informal)
[Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?](https://aclanthology.org/2022.wmt-1.15) (Alabi et al., WMT 2022)
- Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation? (Alabi et al., WMT 2022)
ACL
- Jesujoba Alabi, Lydia Nishimwe, Benjamin Muller, Camille Rey, Benoît Sagot, and Rachel Bawden. 2022. Inria-ALMAnaCH at WMT 2022: Does Transcription Help Cross-Script Machine Translation?. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 233–243, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.