Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion

Ryan Cotterell, John Sylak-Glassman, Christo Kirov


Abstract
Many of the world’s languages contain an abundance of inflected forms for each lexeme. A critical task in processing such languages is predicting these inflected forms. We develop a novel statistical model for the problem, drawing on graphical modeling techniques and recent advances in deep learning. We derive a Metropolis-Hastings algorithm to jointly decode the model. Our Bayesian network draws inspiration from principal parts morphological analysis. We demonstrate improvements on 5 languages.
Anthology ID:
E17-2120
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
759–765
Language:
URL:
https://aclanthology.org/E17-2120
DOI:
Bibkey:
Cite (ACL):
Ryan Cotterell, John Sylak-Glassman, and Christo Kirov. 2017. Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 759–765, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion (Cotterell et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-2120.pdf