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:
- 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)
- PDF:
- https://preview.aclanthology.org/landing_page/C16-1209.pdf