Evaluating the Diversity, Equity, and Inclusion of NLP Technology: A Case Study for Indian Languages

Simran Khanuja, Sebastian Ruder, Partha Talukdar


Abstract
In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world’s languages, be equitable, i.e., not unduly biased towards any particular language, and be inclusive of all users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluation paradigm that assesses NLP technologies across all three dimensions. While diversity and inclusion have received attention in recent literature, equity is currently unexplored. We propose to address this gap using the Gini coefficient, a well-established metric used for estimating societal wealth inequality. Using our paradigm, we highlight the distressed state of current technologies for Indian (IN) languages (a linguistically large and diverse set, with a varied speaker population), across all three dimensions. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation, and more importantly, propose a novel, generalisable approach to optimal resource allocation during fine-tuning. Finally, we discuss steps to mitigate these biases and encourage the community to employ multi-faceted evaluation when building linguistically diverse and equitable technologies.
Anthology ID:
2023.findings-eacl.131
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1763–1777
Language:
URL:
https://aclanthology.org/2023.findings-eacl.131
DOI:
10.18653/v1/2023.findings-eacl.131
Bibkey:
Cite (ACL):
Simran Khanuja, Sebastian Ruder, and Partha Talukdar. 2023. Evaluating the Diversity, Equity, and Inclusion of NLP Technology: A Case Study for Indian Languages. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1763–1777, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Evaluating the Diversity, Equity, and Inclusion of NLP Technology: A Case Study for Indian Languages (Khanuja et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.131.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.131.mp4