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Title: Explaining patterns of fusion in morphological paradigms using the memory-surprisal tradeoff
Languages often express grammatical information through inflectional morphology, in which grammatical features are grouped into strings of morphemes. In this work, we propose that cross-linguistic generalizations about morphological fusion, in which multiple features are expressed through one morpheme, can be explained in part by optimization of processing efficiency, as formalized using the memory--surprisal tradeoff of Hahn et al. (2021). We show in a toy setting that fusion of highly informative neighboring morphemes can lead to greater processing efficiency under our processing model. Next, based on paradigm and frequency data from four languages, we consider both total fusion and gradable fusion using empirical measures developed by Rathi et al. (2021), and find that the degree of fusion is predicted by closeness of optimal morpheme ordering as determined by optimization of processing efficiency. Finally, we show that optimization of processing efficiency can successfully predict typological patterns involving suppletion.  more » « less
Award ID(s):
1947307
PAR ID:
10336254
Author(s) / Creator(s):
Editor(s):
J. Culbertson, A. Perfors
Date Published:
Journal Name:
Proceedings of the 44th Annual Conference of the Cognitive Science Society
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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