Learning reduplication with 2-way finite-state transducers

Hossep Dolatian, Jeffrey Heinz
Proceedings of The 14th International Conference on Grammatical Inference 2018, PMLR 93:67-80, 2019.

Abstract

Reduplication is a cross-linguistically common and productive word-formation 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 finite-state 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 2-way finite-state transducers. Based on this finite-state representation, we identify a subclass of 2-way FSTs based on copying and Output Strictly Local functions. These so-called Concatenated Output Strictly Local functions (C-OSL) 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 C-OSL functions under the assumption that function concatenation is overtly marked.

Cite this Paper

BibTeX
@InProceedings{pmlr-v93-dolatian19a, title = {Learning reduplication with 2-way finite-state transducers}, author = {Dolatian, Hossep and Heinz, Jeffrey}, booktitle = {Proceedings of The 14th International Conference on Grammatical Inference 2018}, pages = {67--80}, year = {2019}, editor = {Unold, Olgierd and Dyrka, Witold and Wieczorek, Wojciech}, volume = {93}, series = {Proceedings of Machine Learning Research}, month = {feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v93/dolatian19a/dolatian19a.pdf}, url = {https://proceedings.mlr.press/v93/dolatian19a.html}, abstract = {Reduplication is a cross-linguistically common and productive word-formation 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 finite-state 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 2-way finite-state transducers. Based on this finite-state representation, we identify a subclass of 2-way FSTs based on copying and Output Strictly Local functions. These so-called Concatenated Output Strictly Local functions (C-OSL) 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 C-OSL functions under the assumption that function concatenation is overtly marked.} }
Endnote
%0 Conference Paper %T Learning reduplication with 2-way finite-state transducers %A Hossep Dolatian %A Jeffrey Heinz %B Proceedings of The 14th International Conference on Grammatical Inference 2018 %C Proceedings of Machine Learning Research %D 2019 %E Olgierd Unold %E Witold Dyrka %E Wojciech Wieczorek %F pmlr-v93-dolatian19a %I PMLR %P 67--80 %U https://proceedings.mlr.press/v93/dolatian19a.html %V 93 %X Reduplication is a cross-linguistically common and productive word-formation 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 finite-state 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 2-way finite-state transducers. Based on this finite-state representation, we identify a subclass of 2-way FSTs based on copying and Output Strictly Local functions. These so-called Concatenated Output Strictly Local functions (C-OSL) 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 C-OSL functions under the assumption that function concatenation is overtly marked.
APA
Dolatian, H. & Heinz, J.. (2019). Learning reduplication with 2-way finite-state transducers. Proceedings of The 14th International Conference on Grammatical Inference 2018, in Proceedings of Machine Learning Research 93:67-80 Available from https://proceedings.mlr.press/v93/dolatian19a.html.

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