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

arXiv: https://arxiv.org/abs/2206.08978 · HTML: https://mlsanity.com/paper/2206.08978/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 17 of 17)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [Detoxifying Language Models Risks Marginalizing Minority Voices](https://mlsanity.com/paper/2104.06390/) (2021)
- [Challenges in Automated Debiasing for Toxic Language Detection](https://mlsanity.com/paper/2102.00086/) (2021)
- [Does Gender Matter? Towards Fairness in Dialogue Systems](https://mlsanity.com/paper/1910.10486/) (2019)
- [Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model](https://mlsanity.com/paper/2008.06460/) (2020)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English](https://mlsanity.com/paper/1707.00061/) (2017)
- [Demoting Racial Bias in Hate Speech Detection](https://mlsanity.com/paper/2005.12246/) (2020)
- [Investigating African-American Vernacular English in Transformer-Based Text Generation](https://mlsanity.com/paper/2010.02510/) (2020)
- [Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting](https://mlsanity.com/paper/2004.14088/) (2020)
- [Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss](https://mlsanity.com/paper/1604.05529/) (2016)
- [Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation](https://mlsanity.com/paper/1508.02096/) (2015)
- [Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework](https://mlsanity.com/paper/2109.13137/) (2021)
- [Examining Racial Bias in an Online Abuse Corpus with Structural Topic Modeling](https://mlsanity.com/paper/2005.13041/) (2020)

## Built on by (most cited first, 3 of 3)

- [Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals](https://mlsanity.com/paper/2207.10032/) (2022)
- [Performance in a dialectal profiling task of LLMs for varieties of Brazilian Portuguese](https://mlsanity.com/paper/2410.10991/) (2024)
- [Evaluating the Usage of African-American Vernacular English in Large Language Models](https://mlsanity.com/paper/2602.21485/) (2026)

## 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.