A bayesian model for learning SCFGs with discontiguous rules

Authors:
Abby Levenberg;Chris Dyer;Phil Blunsom
Affiliations:
University of Oxford;Carnegie Mellon Univeristy;University of Oxford
Venue:
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Year:
2012

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Abstract

We describe a nonparametric model and corresponding inference algorithm for learning Synchronous Context Free Grammar derivations for parallel text. The model employs a Pitman-Yor Process prior which uses a novel base distribution over synchronous grammar rules. Through both synthetic grammar induction and statistical machine translation experiments, we show that our model learns complex translational correspondences--- including discontiguous, many-to-many alignments---and produces competitive translation results. Further, inference is efficient and we present results on significantly larger corpora than prior work.