What is 'Typological Diversity' in NLP?

Esther Ploeger, Wessel Poelman, Miryam de Lhoneux, Johannes Bjerva

Research output: Working paper/PreprintPreprint

Abstract

The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP. However, these improvements only apply to a small subset of the world's languages. Aiming to extend this, an increasing number of papers aspires to enhance generalizable multilingual performance across languages. To this end, linguistic typology is commonly used to motivate language selection, on the basis that a broad typological sample ought to imply generalization across a broad range of languages. These selections are often described as being 'typologically diverse'. In this work, we systematically investigate NLP research that includes claims regarding 'typological diversity'. We find there are no set definitions or criteria for such claims. We introduce metrics to approximate the diversity of language selection along several axes and find that the results vary considerably across papers. Furthermore, we show that skewed language selection can lead to overestimated multilingual performance. We recommend future work to include an operationalization of 'typological diversity' that empirically justifies the diversity of language samples.
Original languageEnglish
DOIs
Publication statusSubmitted - 6 Feb 2024

Keywords

  • Multilingual NLP
  • Typology
  • NLP
  • Language Models
  • Diversity

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