mlsanityData to 7 Oct 2026

Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English

arXiv 2206.08978 ↗First posted 3 Jun 2022cs.CLcited by 3

Not on the map. Why?

Abstract (arXiv)

Currently, natural language processing (NLP) models proliferate language discrimination leading to potentially harmful societal impacts as a result of biased outcomes. For example, part-of-speech taggers trained on Mainstream American English (MAE) produce non-interpretable results when applied to African American English (AAE) as a result of language features not seen during training. In this work, we incorporate a human-in-the-loop paradigm to gain a better understanding of AAE speakers' behavior and their language use, and highlight the need for dialectal language inclusivity so that native AAE speakers can extensively interact with NLP systems while reducing feelings of disenfranchisement.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 59 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

17 of 59 references.

References · 17

  1. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  2. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 194
  3. 2018Adversarial Removal of Demographic Attributes from Text Datacited by 166
  4. 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
  5. 2021Detoxifying Language Models Risks Marginalizing Minority Voicescited by 106
  6. 2021Challenges in Automated Debiasing for Toxic Language Detectioncited by 95
  7. 2019Does Gender Matter? Towards Fairness in Dialogue Systemscited by 89
  8. 2020Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT modelcited by 87
  9. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  10. 2017Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American Englishcited by 69
  11. 2020Demoting Racial Bias in Hate Speech Detectioncited by 62
  12. 2020Investigating African-American Vernacular English in Transformer-Based Text Generationcited by 52
5 more
  1. 2020Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weightingcited by 41
  2. 2016Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Losscited by 30
  3. 2015Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representationcited by 28
  4. 2021Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Frameworkcited by 19
  5. 2020Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modelingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2022Detecting Harmful Online Conversational Content towards LGBTQIA+ Individualscited by 8
  2. 2024Performance in a dialectal profiling task of LLMs for varieties of Brazilian Portuguesecited by 3
  3. 2026Evaluating the Usage of African-American Vernacular English in Large Language Modelscited by 0