SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models
Abdul Khan, Hrishikesh Kanade, Girish Budhrani, Preet Jhanglani, Jia Xu
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:
- 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", 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. %U https://aclanthology.org/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.