Houda Bouamor, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, Dana Abdulrahim, Ossama Obeid, Salam Khalifa, Fadhl Eryani, Alexander Erdmann, Kemal Oflazer
| Challenge: | Using a corpus of 25 Arabic city dialects and a lexicon of 1,045 concepts, we study 25 cities in a travel domain . focus on cities opens new avenues for research from dialectology to dialect identification and machine translation. |
| Approach: | They present two Arabic language resources that are part of the Multi Arabic Dialect Applications and Resources project. |
| Outcome: | The proposed resources are the first of their kind in terms of their coverage and fine granularity. |
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| Challenge: | Arabic dialects are non-standard varieties of Arabic commonly spoken across the Arab world, but lack standard orthographies. |
| Approach: | They present a corpus of 10,000 sentences from five Arabic city dialects represented in the Conventional Orthography for Dialectal Arabic (CODA) they use a bootstrapping technique to speed up annotation and compare similarity between dialects before and after CODA annotation. |
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Fine-Grained Arabic Dialect Identification (C18-1)
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| Challenge: | Existing work on Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic at most (6-way classification). |
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The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic (2022.lrec-1)
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| Challenge: | Various corpora of various sizes and representing different genres, have been created for various Arabic dialects. |
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You Tweet What You Speak: A City-Level Dataset of Arabic Dialects (L18-1)
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| Challenge: | Existing studies of Arabic dialects have focused on blogs and comments on online news sites, but data on other dialects are costly and limited. |
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A Morphologically Annotated Corpus of Emirati Arabic (L18-1)
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| Challenge: | Emirati Arabic corpus is first large-scale morphologically manually annotated corpus . resources for dialectal Arabic NLP tasks are still lacking compared to those for modern standard Arabic (MSA). |
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Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification (2022.lrec-1)
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| Challenge: | Previous work on Arabic Dialect identification focused on specific dialect levels and labels . since dialectal differences tend to be more subtle relative terms to language differences, the DID task is harder than language identification. |
| Approach: | They propose to define a standard hierarchical schema for Arabic Dialect identification . they map 29 different data sets to this schema and use it to aggregate the data . |
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Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)
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Bashar Talafha, Karima Kadaoui, Samar Magdy, Mariem Habiboullah, Chafei Chafei, Ahmed El-Shangiti, Hiba Zayed, Mohamedou Tourad, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, Hour Mohamed, Fakhraddin Alwajih, Abdelrahman Mohamed, Abdellah El Mekki, El Moatez Billah Nagoudi, Benelhadj Saadia, Hamzah Alsayadi, Walid Al-Dhabyani, Sara Shatnawi, Yasir Ech-chammakhy, Amal Makouar, Yousra Berrachedi, Mustafa Jarrar, Shady Shehata, Ismail Berrada, Muhammad Abdul-Mageed
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Unified Guidelines and Resources for Arabic Dialect Orthography (L18-1)
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Nizar Habash, Fadhl Eryani, Salam Khalifa, Owen Rambow, Dana Abdulrahim, Alexander Erdmann, Reem Faraj, Wajdi Zaghouani, Houda Bouamor, Nasser Zalmout, Sara Hassan, Faisal Al-Shargi, Sakhar Alkhereyf, Basma Abdulkareem, Ramy Eskander, Mohammad Salameh, Hind Saddiki
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Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles (2020.lrec-1)
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| Challenge: | a corpus of Arabic speech recordings has been built to allow comparisons between Arabic and other languages. |
| Approach: | They propose to build a corpus of Arabic speech recordings that can be compared with other languages. |
| Outcome: | The proposed corpus can be used for forensic phonetic research and casework applications. |
Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology (2026.findings-acl)
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| Challenge: | Dialectal Arabic datasets embody a range of domain, dialect, and quality. |
| Approach: | They propose a framework for automatic speech recognition in dialectal Arabic to address the limited data availability encountered in dialects. |
| Outcome: | The proposed framework provides access to 31 datasets covering 14 dialects to better address the limited data availability encountered in dialectal Arabic speech processing. |