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.

翻译：为使自然语言处理（NLP）技术具有广泛适用性、公平性和实用性，需服务于全球各语言中的多元使用者群体，确保公平性（即不对任何特定语言存在过度偏见），并包容所有用户（尤其在计算资源受限的低资源环境下）。本文提出一种从三个维度评估NLP技术的范式。尽管多样性与包容性近年已获学术关注，但公平性维度尚待探索。我们引入社会学中衡量财富不平等的基尼系数（Gini coefficient）填补该空白。基于所提范式，我们揭示了当前针对印度语言（一个语言种类繁多、使用人口差异显著的语系）的技术在上述三个维度上的严峻现状。为改善这些指标，我们论证了区域特定选择在模型构建与数据集创建中的重要性，并提出一种新颖且具普适性的微调阶段最优资源分配方法。最后，我们讨论了缓解这些偏差的可行措施，并呼吁学界在构建语言多样性与公平性技术时采用多维度评估方法。