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Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English
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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.
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Analysis of LLM as a grammatical feature tagger for African American English
LLMs are worse than rule-based and transformer baselines at detecting Habitual Be and Multiple Negation in African American English, and their errors correlate with recency and formality biases.
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