Towards Responsible Natural Language Annotation for the Varieties of Arabic
This work highlights a problem for NLP practitioners and linguists in improving dataset quality for diverse languages, but it is incremental as it builds on existing annotation concerns.
The paper addresses the oversight of cultural and linguistic nuances in NLP dataset annotation, proposing a responsible playbook for polyglossic languages like Arabic, based on a study of Arabic social media content.
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.