Learning reduplication with 2way finitestate transducers
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Proceedings of The 14th International Conference on Grammatical Inference 2018, PMLR 93:6780, 2019.
Abstract
Reduplication is a crosslinguistically common and productive wordformation mechanism. However, there are little to no learning results concerning it. This is partly due to the high computational complexity associated with copying, which often goes beyond standard finitestate technology and partly due to the absence of concrete computational models of reduplicative processes. We show here that reduplication can be modeled accurately and succinctly with 2way finitestate transducers. Based on this finitestate representation, we identify a subclass of 2way FSTs based on copying and Output Strictly Local functions. These socalled Concatenated Output Strictly Local functions (COSL) can model the majority of attested reduplicative processes we have surveyed. We introduce a simple extension to the inference algorithm for OSL functions that trivially leads to a provably correct learning result for COSL functions under the assumption that function concatenation is overtly marked.
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