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arXiv · 2211.07534

High-Resource Methodological Bias in Low-Resource Investigations

Maartje ter Hoeve·David Grangier·Natalie Schluter
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Abstract

The central bottleneck for low-resource NLP is typically regarded to be the quantity of accessible data, overlooking the contribution of data quality. This is particularly seen in the development and evaluation of low-resource systems via down sampling of high-resource language data. In this work we investigate the validity of this approach, and we specifically focus on two well-known NLP tasks for our empirical investigations: POS-tagging and machine translation. We show that down sampling from a high-resource language results in datasets with different properties than the low-resource datasets, impacting the model performance for both POS-tagging and machine translation. Based on these results we conclude that naive down sampling of datasets results in a biased view of how well these systems work in a low-resource scenario.

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Maartje ter Hoeve, David Grangier, Natalie Schluter. 2022-11-14. High-Resource Methodological Bias in Low-Resource Investigations. https://arxiv.org/abs/2211.07534

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