Challenge: QASR is the largest transcribed Arabic speech corpus in the broadcast domain.
Approach: They introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain.
Outcome: The proposed dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel.

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Challenge: Existing algorithms for recognizing borrowed and dialectal sounds are limited to Arabic, a dialect-rich language containing more than 22 major dialects.
Approach: They propose a framework to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages that extends beyond its standard orthographic sound sets.
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Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)

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Challenge: despite recent advances in speech processing, the majority of world languages and dialects remain uncovered.
Approach: They propose to collect and transcribe a new Arabic dataset for eight dialects . they also develop strong baselines exploiting the new dataset .
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DiaSet: An Annotated Dataset of Arabic Conversations (2024.lrec-main)

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Challenge: DiaSet is a dataset of dialectical Arabic speech manually transcribed and annotated for two downstream tasks.
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Outcome: The proposed dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan.
Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition (2025.findings-acl)

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Challenge: Using the Wojood framework, we compare existing Arabic Named Entity Recognition models with domain and dialect divergence and resource scarcity.
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CEASR: A Corpus for Evaluating Automatic Speech Recognition (2020.lrec-1)

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Challenge: Automatic Speech Recognition (ASR) systems are increasingly needed for research and practical applications.
Approach: They propose to use public speech corpora to evaluate the quality of automatic speech recognition (ASR) they calculate an average Word Error Rate (WER) per corpus, per system and per corpor-system pair .
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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.
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ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus (2024.lrec-main)

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Challenge: a corpus of multilingual Arabic-English speech is presented in a new paper . a major bottleneck is the lack of data needed for training NLP models .
Approach: They propose a multilingual multidialectal Arabic-English speech corpus with a set of guidelines for automatic speech recognition.
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Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models (2025.emnlp-main)

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Challenge: Arabic diacritics are typically omitted in written Arabic, leading to ambiguity . authors propose a methodology to analyze and refine a large diacritized corpus .
Approach: They propose a methodology to analyze and refine a large diacritized corpus to improve training quality.
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WojoodRelations: Arabic Relation Extraction Corpus and Modeling (2025.emnlp-main)

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Challenge: Existing work on Arabic RE remains limited due to the language’s rich morphology and syntactic complexity, and the lack of large, high-quality datasets.
Approach: They propose to use WojoodRelations to extract relation relationships from Arabic textual data using relation-aware templates and GPT-Joint to perform relation-based retrieval.
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Masader: Metadata Sourcing for Arabic Text and Speech Data Resources (2022.lrec-1)

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Challenge: Currently, there is no online catalogue for Arabic datasets with annotated attributes . this paper aims to identify the publicly available Arabic dataset and provide a catalogue of them to researchers.
Approach: They propose to create the largest public catalogue for Arabic NLP datasets with 25 attributes and a metadata annotation strategy that could be extended to other languages.
Outcome: The proposed approach could be extended to other languages and regions.

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