The Role of Typological Feature Prediction in NLP and Linguistics

Abstract

Abstract Computational typology has gained traction in the field of Natural Language Processing (NLP) in recent years, as evidenced by the increasing number of papers on the topic and the establishment of a Special Interest Group on the topic (SIGTYP), including the organization of successful workshops and shared tasks. A considerable amount of work in this sub-field is concerned with prediction of typological features, for example, for databases such as the World Atlas of Language Structures (WALS) or Grambank. Prediction is argued to be useful either because (1) it allows for obtaining feature values for relatively undocumented languages, alleviating the sparseness in WALS, in turn argued to be useful for both NLP and linguistics; and (2) it allows us to probe models to see whether or not these typological features are encapsulated in, for example, language representations. In this article, we present a critical stance concerning prediction of typological features, investigating to what extent this line of research is aligned with purported needs—both from the perspective of NLP practitioners, and perhaps more importantly, from the perspective of linguists specialized in typology and language documentation. We provide evidence that this line of research in its current state suffers from a lack of interdisciplinary alignment. Based on an extensive survey of the linguistic typology community, we present concrete recommendations for future research in order to improve this alignment between linguists and NLP researchers, beyond the scope of typological feature prediction.