A Principled Framework for Evaluating on Typologically Diverse Languages
This addresses the challenge of achieving representative language sampling for NLP researchers, though it is incremental as it builds on existing typological diversity concepts.
The paper tackles the problem of evaluating multilingual NLP models by proposing a framework for selecting typologically diverse languages, finding that it consistently retrieves more diverse samples than previous methods and improves generalizability in evaluation.
Beyond individual languages, multilingual natural language processing (NLP) research increasingly aims to develop models that perform well across languages generally. However, evaluating these systems on all the world's languages is practically infeasible. To attain generalizability, representative language sampling is essential. Previous work argues that generalizable multilingual evaluation sets should contain languages with diverse typological properties. However, 'typologically diverse' language samples have been found to vary considerably in this regard, and popular sampling methods are flawed and inconsistent. We present a language sampling framework for selecting highly typologically diverse languages given a sampling frame, informed by language typology. We compare sampling methods with a range of metrics and find that our systematic methods consistently retrieve more typologically diverse language selections than previous methods in NLP. Moreover, we provide evidence that this affects generalizability in multilingual model evaluation, emphasizing the importance of diverse language sampling in NLP evaluation.