Paper: Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing

ACL ID P08-1012
Title Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We combine the strengths of Bayesian mod- eling and synchronous grammar in unsu- pervised learning of basic translation phrase pairs. The structured space of a synchronous grammar is a natural fit for phrase pair proba- bility estimation, though the search space can be prohibitively large. Therefore we explore efficient algorithms for pruning this space that lead to empirically effective results. Incorpo- rating a sparse prior using Variational Bayes, biases the models toward generalizable, parsi- monious parameter sets, leading to significant improvements in word alignment. This pref- erence for sparse solutions together with ef- fective pruning methods forms a phrase align- ment regimen that produces better end-to-end translations than standard word alignment ap- proaches.