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：为使自然语言处理技术具有广泛的适用性、公平性和实用性，需服务于全球语言多样化使用者群体，具备公正性（即不对特定语言存在不当偏见），并包容所有用户——尤其是在计算资源受限的低资源环境中。本文提出一种评估范式，从以上三个维度对NLP技术进行综合评价。尽管包容性和多样性已在近期文献中受到关注，但公平性维度尚未被深入探索。我们引入基尼系数（衡量社会财富不平等的经典指标）填补这一研究空白。通过该范式，我们揭示了当前面向印度语言（一个语言种类丰富、使用者基数差异显著的群体）的技术在上述三个维度的严峻现状。为改进这些指标，我们论证了在模型构建与数据集创建中采用区域性策略的重要性，并创新性地提出一种可泛化的最优资源分配方法用于微调阶段。最后，我们讨论了缓解这些偏差的可行措施，并呼吁学界在开发语言多样性与公平性技术时采用多维度评估体系。