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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/E17-2120.pdf