Evaluating the Diversity, Equity, and Inclusion of NLP Technology: A Case Study for I ndian Languages (2023)

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.

## introduction nlp has seen large advances in recent years driven by the rapid progress in transfer learning @xcite . the benefits of these advances, however, are not equally distributed across the world's languages @xcite and users. while linguistic diversity and inclusion have evolved to be a pressing concern today, measures to quantify these are still lacking. the progress of any field is tightly coupled with its evaluation paradigm and the community is incentivized to work on highly visible metrics and benchmarks. in order for users around the world to reap the benefits of nlp technology, we must move from an evaluation that focuses on optimizing raw performance on available test data to a more holistic user-centric evaluation @xcite . in this paper, we attempt to do so by defining an evaluation framework along three dimensions: diversity, equity, and inclusion. 1 diversity is important as nlp technology should be available to speakers of any language @xcite . to this end, recent work @xcite quantifies diversity of nlp technology across the world's languages by weighing normalized task performance for each language based on its speaker population. equity is key as we should aim to develop technology that does not discriminate against speakers of any particular language @xcite . state-of-the-art multilingual models in fact have been shown to perform much better in languages with access to many pre-training resources @xcite . to measure such performance inequity across languages, we propose to use the gini coefficient @xcite , a measure that has been used to represent the income inequality within social groups. finally, inclusion is a concern as the fact that nlp technology is performant in a given task and language does not mean that it is usable by all. state-of-the-art models are becoming larger and larger @xcite and the low-resource settings of many languages often coincide with constraints on computational resources @xcite . the value a technology provides to a user thus also needs to consider how easily such technol-ogy can be deployed in practice. @xcite quantify this based on a model's runtime efficiency, considering factors like throughput and memory. our proposed paradigm is language and model agnostic making it applicable to an arbitrary set of languages and models. we apply our paradigm to highlight the distressing state of current technologies for indian (in) languages. india is a multilingual society with 1369 rationalized languages and dialects being spoken across the country @xcite . of these, 22 scheduled languages foot_2 spoken by almost 97% of the population hold an official recognition and 121 languages have more than 10,000 speakers. additionally, 21.92% of its population lives below the poverty line @xcite . serving this large varied population justly requires a multi-faceted effort and basing our case study on in languages directs the way forward. we evaluate a range of state-of-the-art models and transfer settings @xcite across four standard downstream tasks: named entity recognition (ner), part-of-speech tagging (pos), natural language inference (nli) and question answering (qa). we observe that region-specific choices, i.e., a) region-specific pre-trained models @xcite ) and b) hindi as the transfer language during fine-tuning, generally yield the best results. in terms of efficiency, we find that smaller models are preferable for easier, syntactic tasks while larger models have the edge on more complex, semantic tasks. our findings, however, also highlight that we are still a long way from building perfectly inclusive and equitable nlp technology. towards bridging this gap, we explore how we can most effectively annotate data for the remaining languages. past work @xcite has relied on heuristic and feature-based approaches to source language selection. in our work, we propose a novel, fully computational approach to model the space of source and target languages, and derive the optimal allocation of a fixed annotation budget to maximize performance on our proposed metrics in a multi-source setting. our contributions are the following: 1) we propose a holistic evaluation paradigm that assesses nlp technology based on their diversity, equity, and inclusion. 2) using this paradigm, we evaluate model capabilities for in languages and quantify their shortcomings. 3) we propose a novel approach to select data for fine-tuning these models with the objective of maximizing performance on the proposed metrics. 4) we discuss steps that must be taken to mitigate these biases and call upon the community to incorporate our evaluation paradigm when building models to track progress towards building linguistically inclusive and diverse technologies. user-centric evaluation at its core, the need for language diversity in technologies is tied to the people it serves. previous work @xcite has highlighted the need for transparent and user-centric leaderboard evaluation, reporting practically relevant statistics such as model size, energy efficiency, and inference latency. it is common for speaker populations of under-represented languages to operate in resource-constrained settings. therefore, in addition to evaluating linguistic diversity, we follow @xcite in computing model efficiency, which serves to assess the inclusivity of these technologies. with regards to linguistic di-versity, @xcite highlight the need for more fine-grained evaluation across languages and introduce language-specific leaderboards. @xcite quantify the value of nlp technology weighed by speaker population and determine utilities of several technologies across the world's languages. @xcite propose strategies for fair and efficient model selection depending on one's application, based on the principles of fairness in economics and social choice theory. indian languages the research community has actively been contributing to the advancement of in nlp by collecting and open-sourcing data @xcite @xcite @xcite , building regionspecific multilingual models @xcite @xcite and creating evaluation benchmarks @xcite foot_3 several of these efforts have been undertaken by ai4bharat foot_4 , a non-profit open-source community that has additionally been working on developing resources for in signed languages (sridhar et al., 2020) and creating keyboards for in scripts. recently, google research india launched a question answering (qa) challenge named chaii foot_5 . microsoft research india has also made significant contributions to in nlp with several efforts directed towards code-mixed language processing foot_6 and building tools and datasets for under-represented languages in india foot_7 . ## diversity, equity and inclusion (dei) there is increasing awareness in society to promote diversity, equity and inclusion in our workforce, wherein such measures have recently been enforced by law @xcite . in the social construct, diversity is defined as "the practice of including the many communities, identities, races, ethnicities, backgrounds, abilities, cultures, and beliefs of the people, including underserved communities", equity refers to "the consistent and systematic fair, just, and impartial treatment of all individuals" and inclusion means "the recognition, and use of the talents and skills of employees of all backgrounds" @xcite . given the ubiquitous use of technology in our daily lives, we as technology makers hold the responsibility of making sure all voices are heard and equally represented in the technology we serve. given that our research community is incentivized to work on highly visible metrics and benchmarks, an important first step is to encourage evaluation along these dimensions. previous work mainly focused on average performance (as measured by accuracy or f1 for nlu tasks), which is not indicative of differences in dei. hence, while models claim state of the art based on an increase in average performance, this increase may only be due to making the "rich richer" (see we propose an evaluation paradigm for current nlp technology that operationalizes the wellestablished diversity, equity and inclusion pillars on a language level: we quantify diversity based on the value diverse speaker populations derive from a technology, equity based on egalitarian performance across speaker populations, and inclusion based on a technology's accessibility. we employ metrics of @xcite and @xcite to measure diversity and inclusion respectively and propose a new metric to quantify equity. we describe the metrics in more detail below: ## egalitarian annotation budget allocation model development involves not just the design of an architecture or training but also data annotation. the proposed dimensions thus cannot only be used to assess models but can also inform how data should be annotated across many languages. as fine-tuning on a few labeled examples in the target language has shown to improve zero-shot transfer performance, we study how to allocate an annotation budget across a number of source languages s in order to optimize for inclusion and equity across a set of target languages t. previous work employs a feature-based approach to select a single source language to maximize performance on a target language @xcite or labels examples across all source languages equally @xcite . we propose a fully computational approach for modeling the space of source and target languages for a multi-source multi-target language setting. this is done by empirically estimating performance of language t ϵ t on a held-out set when fine-tuned on x labeled instances of language s ϵ s, ∀(s, t) pairs, which follows a power-law distribution @xcite . we now seek to find the optimal allocation {x s : @xmath0.5). we follow a simple greedy approach to solve this constrained optimization problem as shown in ## discussion building evaluation datasets having uncovered the linguistic inequity and exclusivity of current nlp technologies, we seek to identify practical measures we can take in order to mitigate these biases. as a first step, it is paramount to build representative evaluation sets for all languages as they are required to accurately measure diversity and equity. out of the 23 languages in our case study, most do not have evaluation data across tasks despite holding official recognition and being spoken by 97% of the population. in light of the benefits of an egalitarian data distribution during fewshot learning, we also recommend the collection of small amounts of data across many languages for training, in order to maximize marginal gain. these datasets should be collected at the grassroots level, involving the community they need to serve to capture culturally relevant phenomenon. a prime example of this is the masakhane organisation 13 steering efforts towards data collection in 13 https://www.masakhane.io/ african languages, involving the local community. incentivizing rural, low-income workers to provide for such data also serves as a viable source of supplementary income, and does not degrade dataset quality @xcite . ## conclusion we have proposed a framework for the evaluation of nlp technology based on diversity, equity, and inclusion and proposed the gini coefficient to quantify equity. we have assessed to what extent several modeling and data choices affect the value nlp technology confers to speakers of indian languages. we have also proposed an algorithmic method for resource allocation for task-specific fine-tuning, which outperforms a purely egalitarian distribution of data labeling. finally, we highlight the importance of building representative evaluation sets from the grass-roots level to enable tracking progress, and discuss how even with the best modeling strategies, we have a long road ahead in building inclusive, equitable systems. while regionspecific choices help to a certain extent, building a single global multilingual model without compro-mising on the three metrics is something we should move towards in the future. we sincerely hope our evaluation paradigm aids in tracking the community's progress in building linguistically diverse technologies.