Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data
Akshat Gahoi, Jayant Duneja, Anshul Padhi, Shivam Mangale, Saransh Rajput, Tanvi Kamble, Dipti Sharma, Vasudev Varma
Abstract
Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of EMNLP 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script. To our knowledge, we achieved one of the top ROUGE-L and WER scores for the first task of Monolingual to Code-Mixed machine translation. In this paper, we discuss the use of mBART with some special pre-processing and post-processing (transliteration from Devanagari to Roman) for the first task in detail and the experiments that we performed for the second task of translating code-mixed Hinglish to monolingual English.- Anthology ID:
- 2022.wmt-1.112
- 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:
- 1126–1130
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.112
- DOI:
- Bibkey:
- Cite (ACL):
- Akshat Gahoi, Jayant Duneja, Anshul Padhi, Shivam Mangale, Saransh Rajput, Tanvi Kamble, Dipti Sharma, and Vasudev Varma. 2022. Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1126–1130, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data (Gahoi et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.112.pdf
Export citation
@inproceedings{gahoi-etal-2022-gui,
title = "Gui at {M}ix{MT} 2022 : {E}nglish-{H}inglish : An {MT} Approach for Translation of Code Mixed Data",
author = "Gahoi, Akshat and
Duneja, Jayant and
Padhi, Anshul and
Mangale, Shivam and
Rajput, Saransh and
Kamble, Tanvi and
Sharma, Dipti and
Varma, Vasudev",
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-1.112",
pages = "1126--1130",
abstract = "Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of EMNLP 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script. To our knowledge, we achieved one of the top ROUGE-L and WER scores for the first task of Monolingual to Code-Mixed machine translation. In this paper, we discuss the use of mBART with some special pre-processing and post-processing (transliteration from Devanagari to Roman) for the first task in detail and the experiments that we performed for the second task of translating code-mixed Hinglish to monolingual English.",
}
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%0 Conference Proceedings %T Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data %A Gahoi, Akshat %A Duneja, Jayant %A Padhi, Anshul %A Mangale, Shivam %A Rajput, Saransh %A Kamble, Tanvi %A Sharma, Dipti %A Varma, Vasudev %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 gahoi-etal-2022-gui %X Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of EMNLP 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. The first task dealt with both Roman and Devanagari script as we had monolingual data in both English and Hindi whereas the second task only had data in Roman script. To our knowledge, we achieved one of the top ROUGE-L and WER scores for the first task of Monolingual to Code-Mixed machine translation. In this paper, we discuss the use of mBART with some special pre-processing and post-processing (transliteration from Devanagari to Roman) for the first task in detail and the experiments that we performed for the second task of translating code-mixed Hinglish to monolingual English. %U https://aclanthology.org/2022.wmt-1.112 %P 1126-1130
Markdown (Informal)
[Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data](https://aclanthology.org/2022.wmt-1.112) (Gahoi et al., WMT 2022)
- Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data (Gahoi et al., WMT 2022)
ACL
- Akshat Gahoi, Jayant Duneja, Anshul Padhi, Shivam Mangale, Saransh Rajput, Tanvi Kamble, Dipti Sharma, and Vasudev Varma. 2022. Gui at MixMT 2022 : English-Hinglish : An MT Approach for Translation of Code Mixed Data. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1126–1130, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.