NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task

• URL: http://arxiv.org/abs/2509.02038v2
• Date: Wed, 03 Sep 2025 22:50:00 GMT
• Title: NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task
• Authors: Bashar Talafha, Hawau Olamide Toyin, Peter Sullivan, AbdelRahim Elmadany, Abdurrahman Juma, Amirbek Djanibekov, Chiyu Zhang, Hamad Alshehhi, Hanan Aldarmaki, Mustafa Jarrar, Nizar Habash, Muhammad Abdul-Mageed,
• Abstract summary: We present the findings of the sixth Nuanced Arabic Dialect Identification (ADIN 2025) Shared Task.<n>It focused on Arabic speech dialect processing across three subtasks: spoken dialect identification, speech recognition, and diacritic restoration.<n>The best-performing systems achieved 79.8% accuracy on Subtask 1, 35.68/12.20 WER/CER (overall average) on Subtask 2, and 55/13 WER/CER on Subtask 3.
• Score: 34.40587614887153
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present the findings of the sixth Nuanced Arabic Dialect Identification (NADI 2025) Shared Task, which focused on Arabic speech dialect processing across three subtasks: spoken dialect identification (Subtask 1), speech recognition (Subtask 2), and diacritic restoration for spoken dialects (Subtask 3). A total of 44 teams registered, and during the testing phase, 100 valid submissions were received from eight unique teams. The distribution was as follows: 34 submissions for Subtask 1 "five teams{\ae}, 47 submissions for Subtask 2 "six teams", and 19 submissions for Subtask 3 "two teams". The best-performing systems achieved 79.8% accuracy on Subtask 1, 35.68/12.20 WER/CER (overall average) on Subtask 2, and 55/13 WER/CER on Subtask 3. These results highlight the ongoing challenges of Arabic dialect speech processing, particularly in dialect identification, recognition, and diacritic restoration. We also summarize the methods adopted by participating teams and briefly outline directions for future editions of NADI.

Related papers

• CardiffNLP at CLEARS-2025: Prompting Large Language Models for Plain Language and Easy-to-Read Text Rewriting [49.4237054647147]
  This paper details the CardiffNLP team's contribution to the CLEARS shared task on Spanish text adaptation.<n>We detail our numerous prompt variations, examples, and experimental results.
  arXiv  Detail & Related papers  (2025-08-05T09:16:19Z)
• GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human [71.42669028683741]
  We present a shared task on binary machine generated text detection conducted as a part of the GenAI workshop at COLING 2025.<n>The task consists of two subtasks: Monolingual (English) and Multilingual.<n>We provide a comprehensive overview of the data, a summary of the results, detailed descriptions of the participating systems, and an in-depth analysis of submissions.
  arXiv  Detail & Related papers  (2025-01-19T11:11:55Z)
• NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task [28.40134178913119]
  We describe the findings of the fifth Nuanced Arabic Dialect Identification Shared Task (NADI 2024) NADI 2024 targeted both dialect identification cast as a multi-label task and identification of the Arabic level of dialectness. Winning teams achieved 50.57 Ftextsubscript1 on Subtask1, 0.1403 RMSE for Subtask2, and 20.44 BLEU in Subtask3, respectively.
  arXiv  Detail & Related papers  (2024-07-06T01:18:58Z)
• SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection [68.858931667807]
  Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine.
  arXiv  Detail & Related papers  (2024-04-22T13:56:07Z)
• SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF) [61.49972925493912]
  SemEval-2024 Task 10 is a shared task centred on identifying emotions in code-mixed dialogues. This task comprises three distinct subtasks - emotion recognition in conversation for code-mixed dialogues, emotion flip reasoning for code-mixed dialogues, and emotion flip reasoning for English dialogues. A total of 84 participants engaged in this task, with the most adept systems attaining F1-scores of 0.70, 0.79, and 0.76 for the respective subtasks.
  arXiv  Detail & Related papers  (2024-02-29T08:20:06Z)
• NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task [28.986040897360336]
  We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023) NADI 2023 targeted both dialect identification (Subtask 1) and dialect-to-MSA machine translation (Subtask 2 and Subtask 3). We describe the methods employed by the participating teams and briefly offer an outlook for NADI.
  arXiv  Detail & Related papers  (2023-10-24T18:41:24Z)
• Findings of the WMT 2022 Shared Task on Translation Suggestion [63.457874930232926]
  We report the result of the first edition of the WMT shared task on Translation Suggestion. The task aims to provide alternatives for specific words or phrases given the entire documents generated by machine translation (MT) It consists two sub-tasks, namely, the naive translation suggestion and translation suggestion with hints.
  arXiv  Detail & Related papers  (2022-11-30T03:48:36Z)
• NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task [16.688997360734472]
  Third Nuanced Arabic Dialect Identification Shared Task (NADI 2022) NADI 2022 targeted both dialect identification (Subtask 1) and dialectal sentiment analysis (Subtask 2) at the country level. Winning team achieved 27.06 F1 on Subtask 1 and F1=75.16 on Subtask 2.
  arXiv  Detail & Related papers  (2022-10-18T04:31:05Z)
• NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task [20.34810224205086]
  This Shared Task includes four subtasks: country-level Modern Standard Arabic (MSA) identification (Subtask 1.1), country-level dialect identification (Subtask 1.2), and province-level sub-dialect identification (Subtask 2.1) The dataset covers a total of 100 provinces from 21 Arab countries, collected from the Twitter domain. A total of 53 teams from 23 countries registered to participate in the tasks, thus reflecting the interest of the community in this area.
  arXiv  Detail & Related papers  (2021-03-04T04:59:37Z)
• NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task [18.23153068720659]
  We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI) Data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain. NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level.
  arXiv  Detail & Related papers  (2020-10-21T22:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.