@inproceedings{abdel-salam-2023-rematchka-nadi,
title = "rematchka at {NADI} 2023 shared task: Parameter Efficient tuning for Dialect Identification and Dialect Machine Translation",
author = "Abdel-Salam, Reem",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.70",
doi = "10.18653/v1/2023.arabicnlp-1.70",
pages = "652--657",
abstract = "Dialect identification systems play a significant role in various fields and applications as in speech and language technologies, facilitating language education, supporting sociolinguistic research, preserving linguistic diversity, enhancing text-to-speech systems. In this paper, we provide our findings and results in NADI 2023 shared task for country-level dialect identification and machine translation (MT) from dialect to MSA. The proposed models achieved an F1-score of 86.18 at the dialect identification task, securing second place in first subtask. Whereas for the machine translation task, the submitted model achieved a BLEU score of 11.37 securing fourth and third place in second and third subtask. The proposed model utilizes parameter efficient training methods which achieves better performance when compared to conventional fine-tuning during the experimentation phase.",
}
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<namePart type="given">Nizar</namePart>
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%0 Conference Proceedings
%T rematchka at NADI 2023 shared task: Parameter Efficient tuning for Dialect Identification and Dialect Machine Translation
%A Abdel-Salam, Reem
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F abdel-salam-2023-rematchka-nadi
%X Dialect identification systems play a significant role in various fields and applications as in speech and language technologies, facilitating language education, supporting sociolinguistic research, preserving linguistic diversity, enhancing text-to-speech systems. In this paper, we provide our findings and results in NADI 2023 shared task for country-level dialect identification and machine translation (MT) from dialect to MSA. The proposed models achieved an F1-score of 86.18 at the dialect identification task, securing second place in first subtask. Whereas for the machine translation task, the submitted model achieved a BLEU score of 11.37 securing fourth and third place in second and third subtask. The proposed model utilizes parameter efficient training methods which achieves better performance when compared to conventional fine-tuning during the experimentation phase.
%R 10.18653/v1/2023.arabicnlp-1.70
%U https://aclanthology.org/2023.arabicnlp-1.70
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.70
%P 652-657
Markdown (Informal)
[rematchka at NADI 2023 shared task: Parameter Efficient tuning for Dialect Identification and Dialect Machine Translation](https://aclanthology.org/2023.arabicnlp-1.70) (Abdel-Salam, ArabicNLP-WS 2023)
ACL