{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T21:17:37Z","timestamp":1764019057523,"version":"3.41.0"},"reference-count":29,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>Translation from the mother tongue, including the Tunisian dialect, to modern standard Arabic is a highly significant field in natural language processing due to its wide range of applications and associated benefits. Recently, researchers have shown increased interest in the Tunisian dialect, primarily driven by the massive volume of content generated spontaneously by Tunisians on social media following the revolution. This article presents two distinct translators for converting the Tunisian dialect into Modern Standard Arabic. The first translator utilizes a rule-based approach, employing a collection of finite state transducers and a bilingual dictionary derived from the study corpus. On the other hand, the second translator relies on deep learning models, specifically the sequence-to-sequence transformer model and a parallel corpus. To assess, evaluate, and compare the performance of the two translators, we conducted tests using a parallel corpus comprising 8,599 words. The results achieved by both translators are noteworthy. The translator based on finite state transducers achieved a BLEU score of 56.65, while the transformer model-based translator achieved a higher score of 66.07.<\/jats:p>","DOI":"10.1145\/3681788","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T11:19:20Z","timestamp":1721819960000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Translation from Tunisian Dialect to Modern Standard Arabic: Exploring Finite-State Transducers and Sequence-to-Sequence Transformer Approaches"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0585-0795","authenticated-orcid":false,"given":"Roua","family":"Torjmen","sequence":"first","affiliation":[{"name":"Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5972-9694","authenticated-orcid":false,"given":"Kais","family":"Haddar","sequence":"additional","affiliation":[{"name":"Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Sina Ahmadi and Maraim Masoud. 2020. Towards machine translation for the kurdish language. In Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages Alina Karakanta Atul Kr. Ojha Chao-Hong Liu Jade Abbott John Ortega Jonathan Washington Nathaniel Oco Surafel Melaku Lakew Tommi A. Pirinen Valentin Malykh Varvara Logacheva and Xiaobing Zhao (Eds.). Association for Computational Linguistics Suzhou China 87\u201398. https:\/\/aclanthology.org\/2020.loresmt-1.12"},{"key":"e_1_3_2_3_2","first-page":"173","volume-title":"Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS)","author":"Al-Ibrahim Roqayah","year":"2020","unstructured":"Roqayah Al-Ibrahim and Rehab M. Duwairi. 2020. Neural machine translation from Jordanian dialect to Modern Standard Arabic. In Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS). IEEE, 173\u2013178."},{"key":"e_1_3_2_4_2","unstructured":"Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E. Hinton. 2016. Layer normalization. arXiv:1607.06450 [stat.ML] https:\/\/arxiv.org\/abs\/1607.06450"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7534712"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85980-2_21"},{"key":"e_1_3_2_7_2","first-page":"1240","volume-title":"Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014","author":"Bouamor Houda","year":"2014","unstructured":"Houda Bouamor, Nizar Habash, and Kemal Oflazer. 2014. A multidialectal parallel corpus of Arabic. In Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014, Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asunci\u00f3n Moreno, Jan Odijk, and Stelios Piperidis (Eds.). 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European Language Resources Association (ELRA)."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.3115\/V1\/D14-1179"},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Injy Hamed Nizar Habash Slim Abdennadher and Ngoc Thang Vu. 2023. Investigating lexical replacements for arabic-english code-switched data augmentation. (2023). arXiv:2205.12649 [cs.CL] https:\/\/arxiv.org\/abs\/2205.12649","DOI":"10.18653\/v1\/2023.loresmt-1.7"},{"key":"e_1_3_2_11_2","series-title":"Communications in Computer and Information Science","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/978-3-319-55002-2_8","volume-title":"Proceedings of the 10th International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ. NooJ 2016, Revised Selected Papers","volume":"667","author":"Hammouda Nadia Ghezaiel","year":"2016","unstructured":"Nadia Ghezaiel Hammouda and Kais Haddar. 2016. Integration of a segmentation tool for Arabic corpora in NooJ platform to build an automatic annotation tool. In Proceedings of the 10th International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ. NooJ 2016, Revised Selected Papers, Linda Barone, Mario Monteleone, and Max Silberztein (Eds.). Communications in Computer and Information Science, Vol. 667, Springer, 89\u2013100."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-18111-0_47"},{"key":"e_1_3_2_13_2","volume-title":"Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING)","author":"Harrat Salima","year":"2017","unstructured":"Salima Harrat, Karima Meftouh, and Kamel Smaili. 2017. Creating parallel Arabic dialect corpus: Pitfalls to avoid. 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