CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation
Sahinur Rahman Laskar, Rahul Singh, Shyambabu Pandey, Riyanka Manna, Partha Pakray, Sivaji Bandyopadhyay
Abstract
The mixing of two or more languages in speech or text is known as code-mixing. In this form of communication, users mix words and phrases from multiple languages. Code-mixing is very common in the context of Indian languages due to the presence of multilingual societies. The probability of the existence of code-mixed sentences in almost all Indian languages since in India English is the dominant language for social media textual communication platforms. We have participated in the WMT22 shared task of code-mixed machine translation with the team name: CNLP-NITS-PP. In this task, we have prepared a synthetic Hinglish–English parallel corpus using transliteration of original Hindi sentences to tackle the limitation of the parallel corpus, where, we mainly considered sentences that have named-entity (proper noun) from the available English-Hindi parallel corpus. With the addition of synthetic bi-text data to the original parallel corpus (train set), our transformer-based neural machine translation models have attained recall-oriented understudy for gisting evaluation (ROUGE-L) scores of 0.23815, 0.33729, and word error rate (WER) scores of 0.95458, 0.88451 at Sub-Task-1 (English-to-Hinglish) and Sub-Task-2 (Hinglish-to-English) for test set results respectively.- Anthology ID:
- 2022.wmt-1.116
- 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:
- 1158–1161
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.116
- DOI:
- Bibkey:
- Cite (ACL):
- Sahinur Rahman Laskar, Rahul Singh, Shyambabu Pandey, Riyanka Manna, Partha Pakray, and Sivaji Bandyopadhyay. 2022. CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1158–1161, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation (Laskar et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.116.pdf
Export citation
@inproceedings{laskar-etal-2022-cnlp-nits,
title = "{CNLP}-{NITS}-{PP} at {M}ix{MT} 2022: {H}inglish-{E}nglish Code-Mixed Machine Translation",
author = "Laskar, Sahinur Rahman and
Singh, Rahul and
Pandey, Shyambabu and
Manna, Riyanka and
Pakray, Partha and
Bandyopadhyay, Sivaji",
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-1.116",
pages = "1158--1161",
abstract = "The mixing of two or more languages in speech or text is known as code-mixing. In this form of communication, users mix words and phrases from multiple languages. Code-mixing is very common in the context of Indian languages due to the presence of multilingual societies. The probability of the existence of code-mixed sentences in almost all Indian languages since in India English is the dominant language for social media textual communication platforms. We have participated in the WMT22 shared task of code-mixed machine translation with the team name: CNLP-NITS-PP. In this task, we have prepared a synthetic Hinglish{--}English parallel corpus using transliteration of original Hindi sentences to tackle the limitation of the parallel corpus, where, we mainly considered sentences that have named-entity (proper noun) from the available English-Hindi parallel corpus. With the addition of synthetic bi-text data to the original parallel corpus (train set), our transformer-based neural machine translation models have attained recall-oriented understudy for gisting evaluation (ROUGE-L) scores of 0.23815, 0.33729, and word error rate (WER) scores of 0.95458, 0.88451 at Sub-Task-1 (English-to-Hinglish) and Sub-Task-2 (Hinglish-to-English) for test set results respectively.",
}
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%0 Conference Proceedings %T CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation %A Laskar, Sahinur Rahman %A Singh, Rahul %A Pandey, Shyambabu %A Manna, Riyanka %A Pakray, Partha %A Bandyopadhyay, Sivaji %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 laskar-etal-2022-cnlp-nits %X The mixing of two or more languages in speech or text is known as code-mixing. In this form of communication, users mix words and phrases from multiple languages. Code-mixing is very common in the context of Indian languages due to the presence of multilingual societies. The probability of the existence of code-mixed sentences in almost all Indian languages since in India English is the dominant language for social media textual communication platforms. We have participated in the WMT22 shared task of code-mixed machine translation with the team name: CNLP-NITS-PP. In this task, we have prepared a synthetic Hinglish–English parallel corpus using transliteration of original Hindi sentences to tackle the limitation of the parallel corpus, where, we mainly considered sentences that have named-entity (proper noun) from the available English-Hindi parallel corpus. With the addition of synthetic bi-text data to the original parallel corpus (train set), our transformer-based neural machine translation models have attained recall-oriented understudy for gisting evaluation (ROUGE-L) scores of 0.23815, 0.33729, and word error rate (WER) scores of 0.95458, 0.88451 at Sub-Task-1 (English-to-Hinglish) and Sub-Task-2 (Hinglish-to-English) for test set results respectively. %U https://aclanthology.org/2022.wmt-1.116 %P 1158-1161
Markdown (Informal)
[CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation](https://aclanthology.org/2022.wmt-1.116) (Laskar et al., WMT 2022)
- CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation (Laskar et al., WMT 2022)
ACL
- Sahinur Rahman Laskar, Rahul Singh, Shyambabu Pandey, Riyanka Manna, Partha Pakray, and Sivaji Bandyopadhyay. 2022. CNLP-NITS-PP at MixMT 2022: Hinglish-English Code-Mixed Machine Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1158–1161, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.