Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion


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.

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics