The Past, Present, and Future of Typological Databases in NLP
Submission Type: Regular Short Paper
Submission Track: Theme Track: Large Language Models and the Future of NLP
Submission Track 2: Linguistic Theories, Cognitive Modeling, and Psycholinguistics
Keywords: typology, typological feature prediction, large language models
TL;DR: We investigate mismatches and errors in typological databases, related this to the current state of NLP, and argue for awareness of this issue.
Abstract: 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.
Submission Number: 3228
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