Abstract
We expect generative models of language to correctly predict surface forms from underlying forms, but morphologically complex words, especially compounds, can exhibit idiosyncratic outputs, which require an extra lexical listing. This results in (a) a poorer Minimum Description Length of our model and (b) failure of a grammar to capture patterning among exceptions. To solve an instance of this problem, we examine pitch-accent patterns of 2-mora-2-mora Japanese Yamato (native) noun-noun compounds, hitherto considered semi-predictable but which show gradient tendencies among constituents to trigger a particular accent pattern. In the framework of Gradient Symbolic Computation (Smolensky, Paul & Matthew Goldrick. 2015. Gradient symbolic computation. LSA Summer Institute Workshop. Chicago.), a type of harmonic grammar which allows partially activated feature values and weighted constraints, such gradient patterns can be captured through the additive combination of coalescing features on each conjunct, which results in a pitch accent when the summed activations surpass a threshold determined by the grammar. The ability of this framework to completely predict these semi-regular patterns holds promise that it can also explain similar kinds of patterns in other languages.
Acknowledgement
Helpful discussion and suggestions were provided by Paul Smolensky, Matthew Goldrick, Armin Mester and participants at the Strength in Grammar workshop at the University of Leipzig where my attendance was generously supported by NSF grant BCS1344269. I also thank Shigeto Kawahara and two anonymous reviewers at Linguistics Vanguard for helpful comments and suggestions. All errors are my own.
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