The Past, Present, and Future of Typological Databases in NLP
This addresses a problem for NLP researchers working with low-resource languages, but it is incremental as it builds on existing linguistic recommendations.
The paper tackles inconsistencies in typological databases like WALS and Grambank, which hinder their use in NLP for low-resource languages, by systematically exploring disagreements and advocating for a continuous view of typological features to improve future applications.
Typological information has the potential to be beneficial in the development of NLP models, particularly for low-resource languages. Unfortunately, current large-scale typological databases, notably WALS and Grambank, are inconsistent both with each other and with other sources of typological information, such as linguistic grammars. Some of these inconsistencies stem from coding errors or linguistic variation, but many of the disagreements are due to the discrete categorical nature of these databases. We shed light on this issue by systematically exploring disagreements across typological databases and resources, and their uses in NLP, covering the past and present. We next investigate the future of such work, offering an argument that a continuous view of typological features is clearly beneficial, echoing recommendations from linguistics. We propose that such a view of typology has significant potential in the future, including in language modeling in low-resource scenarios.