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

Domaine:

natural language processing

Type de record:

paper
Créateur:
Dac
Éditeur:
arXiv
Hôte:avatar
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. Accepted to the NAACL 2022 (HCI+NLP) Workshop

Visit

doi.orgarxiv.org

Tags

Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

arXiv.org perpetual, non-exclusive licensehttp://arxiv.org/licenses/nonexclusive-distrib/1.0/

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