@inproceedings{derouich-etal-2023-anlp,
title = "{ANLP}-{RG} at {NADI} 2023 shared task: Machine Translation of {A}rabic Dialects: A Comparative Study of Transformer Models",
author = "Derouich, Wiem and
Kchaou, Sameh and
Boujelbane, Rahma",
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-1.75",
doi = "10.18653/v1/2023.arabicnlp-1.75",
pages = "683--689",
abstract = "In this paper, we present our findings within the context of the NADI-2023 Shared Task (Subtask 2). Our task involves developing a translation model from the Palestinian, Jordanian, Emirati, and Egyptian dialects to Modern Standard Arabic (MSA) using the MADAR parallel corpus, even though it lacks a parallel subset for the Emirati dialect. To address this challenge, we conducted a comparative analysis, evaluating the fine-tuning results of various transformer models using the MADAR corpus as a learning resource. Additionally, we assessed the effectiveness of existing translation tools in achieving our translation objectives. The best model achieved a BLEU score of 11.14{\%} on the dev set and 10.02 on the test set.",
}
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<abstract>In this paper, we present our findings within the context of the NADI-2023 Shared Task (Subtask 2). Our task involves developing a translation model from the Palestinian, Jordanian, Emirati, and Egyptian dialects to Modern Standard Arabic (MSA) using the MADAR parallel corpus, even though it lacks a parallel subset for the Emirati dialect. To address this challenge, we conducted a comparative analysis, evaluating the fine-tuning results of various transformer models using the MADAR corpus as a learning resource. Additionally, we assessed the effectiveness of existing translation tools in achieving our translation objectives. The best model achieved a BLEU score of 11.14% on the dev set and 10.02 on the test set.</abstract>
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%0 Conference Proceedings
%T ANLP-RG at NADI 2023 shared task: Machine Translation of Arabic Dialects: A Comparative Study of Transformer Models
%A Derouich, Wiem
%A Kchaou, Sameh
%A Boujelbane, Rahma
%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 derouich-etal-2023-anlp
%X In this paper, we present our findings within the context of the NADI-2023 Shared Task (Subtask 2). Our task involves developing a translation model from the Palestinian, Jordanian, Emirati, and Egyptian dialects to Modern Standard Arabic (MSA) using the MADAR parallel corpus, even though it lacks a parallel subset for the Emirati dialect. To address this challenge, we conducted a comparative analysis, evaluating the fine-tuning results of various transformer models using the MADAR corpus as a learning resource. Additionally, we assessed the effectiveness of existing translation tools in achieving our translation objectives. The best model achieved a BLEU score of 11.14% on the dev set and 10.02 on the test set.
%R 10.18653/v1/2023.arabicnlp-1.75
%U https://aclanthology.org/2023.arabicnlp-1.75
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.75
%P 683-689
Markdown (Informal)
[ANLP-RG at NADI 2023 shared task: Machine Translation of Arabic Dialects: A Comparative Study of Transformer Models](https://aclanthology.org/2023.arabicnlp-1.75) (Derouich et al., ArabicNLP-WS 2023)
ACL