@inproceedings{dacon-2022-towards,
title = "Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of {A}frican-{A}merican {E}nglish",
author = "Dacon, Jamell",
editor = "Blodgett, Su Lin and
Daum{\'e} III, Hal and
Madaio, Michael and
Nenkova, Ani and
O'Connor, Brendan and
Wallach, Hanna and
Yang, Qian",
booktitle = "Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.hcinlp-1.8",
doi = "10.18653/v1/2022.hcinlp-1.8",
pages = "55--63",
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 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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<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 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.</abstract>
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%0 Conference Proceedings
%T Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English
%A Dacon, Jamell
%Y Blodgett, Su Lin
%Y Daumé III, Hal
%Y Madaio, Michael
%Y Nenkova, Ani
%Y O’Connor, Brendan
%Y Wallach, Hanna
%Y Yang, Qian
%S Proceedings of the Second Workshop on Bridging Human–Computer Interaction and Natural Language Processing
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F dacon-2022-towards
%X 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.
%R 10.18653/v1/2022.hcinlp-1.8
%U https://aclanthology.org/2022.hcinlp-1.8
%U https://doi.org/10.18653/v1/2022.hcinlp-1.8
%P 55-63
Markdown (Informal)
[Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English](https://aclanthology.org/2022.hcinlp-1.8) (Dacon, HCINLP 2022)
ACL