Data-driven learning of symbolic constraints for a log-linear model in a phonological setting

Gabriel Doyle, Roger Levy


Abstract
We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.
Anthology ID:
C16-1209
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2217–2226
Language:
URL:
https://aclanthology.org/C16-1209
DOI:
Bibkey:
Cite (ACL):
Gabriel Doyle and Roger Levy. 2016. Data-driven learning of symbolic constraints for a log-linear model in a phonological setting. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2217–2226, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Data-driven learning of symbolic constraints for a log-linear model in a phonological setting (Doyle & Levy, COLING 2016)
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PDF:
https://aclanthology.org/C16-1209.pdf