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Towards Responsible Natural Language Annotation for the Varieties of Arabic

arXiv 2203.09597 ↗First posted 17 Mar 2022cs.CL cs.CYcited by 14

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Abstract (arXiv)

When building NLP models, there is a tendency to aim for broader coverage, often overlooking cultural and (socio)linguistic nuance. In this position paper, we make the case for care and attention to such nuances, particularly in dataset annotation, as well as the inclusion of cultural and linguistic expertise in the process. We present a playbook for responsible dataset creation for polyglossic, multidialectal languages. This work is informed by a study on Arabic annotation of social media content.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

11 of 40 references.

References · 11

  1. 2018Model Cards for Model Reportingcited by 1,068
  2. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  3. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 388
  4. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  5. 2019Does Object Recognition Work for Everyone?cited by 149
  6. 2020Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamicscited by 31
  7. 2019Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligencecited by 21
  8. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 20
  9. 2021Institutionalising Ethics in AI through Broader Impact Requirementscited by 20
  10. 2019Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learningcited by 12
  11. 2020Non-portability of Algorithmic Fairness in Indiacited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 3; 2026: 3.

14 papers.

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  1. 2023Lost in Translation: Large Language Models in Non-English Content Analysiscited by 34
  2. 2023ALDi: Quantifying the Arabic Level of Dialectness of Textcited by 24
  3. 2023Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluationcited by 18
  4. 2024ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?cited by 17
  5. 2024AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabiccited by 11
  6. 2025LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones?cited by 6
  7. 2024Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasetscited by 4
  8. 2023Toxic language detection: a systematic review of Arabic datasetscited by 3
  9. 2025Revisiting Common Assumptions about Arabic Dialects in NLPcited by 2
  10. 2025DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Modelscited by 1
  11. 2026AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translationcited by 1
  12. 2024Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic Hate Speech Detectioncited by 0
2 more
  1. 2026Maastricht University at AMIYA: Adapting LLMs for Dialectal Arabic using Fine-tuning and MBR Decodingcited by 0
  2. 2026SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extractioncited by 0