Work

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

Year: 2022

Type: conference-paper

Abstract: 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 noninterpretable 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.

Source: Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)

DOI: 10.18653/v1/2022.hcinlp-1.8

Author: Jamell Dacon

Institution: Michigan State University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

OpenAlex ID: W4287888404