SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models
Abdul Khan, Hrishikesh Kanade, Girish Budhrani, Preet Jhanglani, Jia Xu
Correct Metadata for
Abstract
This paper describes the Stevens Institute of Technology’s submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish and subtask 2 Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT models and in-domain datasets, as well as back-translation and ensemble techniques. The translation output is automatically evaluated against the reference translations using ROUGE-L and WER. Our system achieves the 1st position on subtask 2 according to ROUGE-L, WER, and human evaluation, 1st position on subtask 1 according to WER and human evaluation, and 3rd position on subtask 1 with respect to ROUGE-L metric.- Anthology ID:
- 2022.wmt-1.114
- 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:
- 1136–1144
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.114/
- DOI:
- 10.18653/v1/2022.wmt-1.114
- Bibkey:
- Cite (ACL):
- Abdul Khan, Hrishikesh Kanade, Girish Budhrani, Preet Jhanglani, and Jia Xu. 2022. SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1136–1144, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models (Khan et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.114.pdf
Export citation
@inproceedings{khan-etal-2022-sit,
title = "{SIT} at {M}ix{MT} 2022: Fluent Translation Built on Giant Pre-trained Models",
author = "Khan, Abdul and
Kanade, Hrishikesh and
Budhrani, Girish and
Jhanglani, Preet and
Xu, Jia",
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-1.114/",
doi = "10.18653/v1/2022.wmt-1.114",
pages = "1136--1144",
abstract = "This paper describes the Stevens Institute of Technology{'}s submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish and subtask 2 Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT models and in-domain datasets, as well as back-translation and ensemble techniques. The translation output is automatically evaluated against the reference translations using ROUGE-L and WER. Our system achieves the 1st position on subtask 2 according to ROUGE-L, WER, and human evaluation, 1st position on subtask 1 according to WER and human evaluation, and 3rd position on subtask 1 with respect to ROUGE-L metric."
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%0 Conference Proceedings %T SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models %A Khan, Abdul %A Kanade, Hrishikesh %A Budhrani, Girish %A Jhanglani, Preet %A Xu, Jia %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 khan-etal-2022-sit %X This paper describes the Stevens Institute of Technology’s submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish and subtask 2 Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT models and in-domain datasets, as well as back-translation and ensemble techniques. The translation output is automatically evaluated against the reference translations using ROUGE-L and WER. Our system achieves the 1st position on subtask 2 according to ROUGE-L, WER, and human evaluation, 1st position on subtask 1 according to WER and human evaluation, and 3rd position on subtask 1 with respect to ROUGE-L metric. %R 10.18653/v1/2022.wmt-1.114 %U https://aclanthology.org/2022.wmt-1.114/ %U https://doi.org/10.18653/v1/2022.wmt-1.114 %P 1136-1144
Markdown (Informal)
[SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models](https://aclanthology.org/2022.wmt-1.114/) (Khan et al., WMT 2022)
- SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models (Khan et al., WMT 2022)
ACL
- Abdul Khan, Hrishikesh Kanade, Girish Budhrani, Preet Jhanglani, and Jia Xu. 2022. SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1136–1144, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.