ACL 2022findings15 citations
Towards Responsible Natural Language Annotation for the Varieties of Arabic
Abstract
When building NLP models, there is a tendency to aim for broader coverage, often overlooking cultural and (socio)linguistic nuance. In this position paper, we make the case for care and attention to such nuances, particularly in dataset annotation, as well as the inclusion of cultural and linguistic expertise in the process. We present a playbook for responsible dataset creation for polyglossic, multidialectal languages. This work is informed by a study on Arabic annotation of social media content.
BibTeX
@inproceedings{bergman-diab-2022-towards,
title = "Towards Responsible Natural Language Annotation for the Varieties of {A}rabic",
author = "Bergman, A. and
Diab, Mona",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.31/",
doi = "10.18653/v1/2022.findings-acl.31",
pages = "364--371"
}