Fit for our purpose, not yours: Benchmark for a low-resource, Indigenous language

Abstract

Influential and popular benchmarks in AI are largely irrelevant to developing NLP tools for low-resource, Indigenous languages. With the primary goal of measuring the performance of general-purpose AI systems, these benchmarks fail to give due consideration and care to individual language communities, especially low-resource languages. The datasets contain numerous grammatical and orthographic errors, poor pronunciation, limited vocabulary, and the content lacks cultural relevance to the language community. To overcome the issues with these benchmarks, we have created a dataset for the M ¯ aori language (the Indigenous language of Aotearoa/New Zealand) to pursue NLP tools that are ‘fit-for-our-purpose’. This paper demonstrates how low-resourced, Indigenous languages can develop tailored, high-quality benchmarks that; i. Reflect the unique characteristics of their language, ii. Reflect the diversity of speakers in the language community, iii. Support the aspirations for the tools they are developing and their language revitalisation efforts. All of which sit within a broader understanding of the impact of colonisation on their language.