mlsanityData to 14 Oct 2026

A Material Lens on Coloniality in NLP

arXiv 2311.08391 ↗First posted 14 Nov 2023cs.CLcited by 21

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Abstract (arXiv)

Coloniality, the continuation of colonial harms beyond "official" colonization, has pervasive effects across society and scientific fields. Natural Language Processing (NLP) is no exception to this broad phenomenon. In this work, we argue that coloniality is implicitly embedded in and amplified by NLP data, algorithms, and software. We formalize this analysis using Actor-Network Theory (ANT): an approach to understanding social phenomena through the network of relationships between human stakeholders and technology. We use our Actor-Network to guide a quantitative survey of the geography of different phases of NLP research, providing evidence that inequality along colonial boundaries increases as NLP builds on itself. Based on this, we argue that combating coloniality in NLP requires not only changing current values but also active work to remove the accumulation of colonial ideals in our foundational data and algorithms.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 71 of its 202 references that are in the map. First posted on arXiv 14 Nov 2023.

Builds on

71 of 202 references.

References · 71

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2020Scaling Laws for Neural Language Modelscited by 6,586
  5. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  6. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  7. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  8. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  9. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  10. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  11. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  12. 2018Model Cards for Model Reportingcited by 1,080
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  1. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  2. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
  3. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 742
  4. 2019How multilingual is Multilingual BERT?cited by 645
  5. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  6. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  7. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
  8. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  9. 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
  10. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  11. 2019MLQA: Evaluating Cross-lingual Extractive Question Answeringcited by 340
  12. 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 333
  13. 2019PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identificationcited by 264
  14. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
  15. 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
  16. 2019Racial Bias in Hate Speech and Abusive Language Detection Datasetscited by 209
  17. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 207
  18. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 194
  19. 2023Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Modelscited by 192
  20. 2021The Values Encoded in Machine Learning Researchcited by 172
  21. 2021XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluationcited by 168
  22. 2022Compute Trends Across Three Eras of Machine Learningcited by 163
  23. 2020The Hardware Lotterycited by 160
  24. 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
  25. 2020MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmarkcited by 158
  26. 2021MasakhaNER: Named Entity Recognition for African Languagescited by 155
  27. 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 149
  28. 2022MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languagescited by 146
  29. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 138
  30. 2020XOR QA: Cross-lingual Open-Retrieval Question Answeringcited by 121
  31. 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 101
  32. 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 98
  33. 2020Translation Artifacts in Cross-lingual Transfer Learningcited by 96
  34. 2023The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AIcited by 73
  35. 2017Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American Englishcited by 71
  36. 2022One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesiacited by 66
  37. 2020Investigating African-American Vernacular English in Transformer-Based Text Generationcited by 53
  38. 2021Beyond Fair Pay: Ethical Implications of NLP Crowdsourcingcited by 53

50 most cited of 71 All 71 (JSON)

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Citing papers per year: 2024: 11; 2025: 5; 2026: 5.

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  5. 2025Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languagescited by 0
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  7. 2026From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Datasetcited by 0
  8. 2026Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?cited by 0
  9. 2026Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectivelycited by 0