Preference Grammars and Decoding Algorithms for Probabilistic Synchronous Context Free Grammar Based Translation.
BibTeX
@MISC{Venugopal_preferencegrammars,
author = {Ashish Venugopal and A. Smith and Alex Waibel and Ashish Venugopal},
title = {Preference Grammars and Decoding Algorithms for Probabilistic Synchronous Context Free Grammar Based Translation.},
year = {}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Probabilistic Synchronous Context-free Grammars (PSCFGs) [Aho and Ullmann, 1969, Wu, 1996] define weighted transduction rules to represent translation and reordering operations. When translation models use features that are defined locally, on each rule, there are efficient dynamic programming algorithms to perform translation with these grammars [Kasami, 1965]. In general, the integration of non-local features into the translation model can make translation NP-hard, requiring decoding approximations that limit the impact of these features. In this thesis, we consider the impact and interaction between two non-local features, the n-gram language model (LM) and labels on rule nonterminal symbols in the Syntax-Augmented MT (SAMT) grammar [Zollmann and Venugopal, 2006]. While these features do not result in NP-hard search, they would lead to serious increases in wall-clock runtime if naïve dynamic programming methods are applied. We develop novel two-pass algorithms that make strong decoding approximations during a first pass search, generating a hypergraph of sentence spanning translation i derivations. In a second pass, we use knowledge about non-local features to explore
