Paper: Augmenting String-to-Tree Translation Models with Fuzzy Use of Source-side Syntax

ACL ID D11-1019
Title Augmenting String-to-Tree Translation Models with Fuzzy Use of Source-side Syntax
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Due to its explicit modeling of the grammaticality of the output via target-side syntax, the string-to-tree model has been shown to be one of the most successful syntax-based translation models. However, a major limitation of this model is that it does not utilize any useful syntactic information on the source side. In this paper, we analyze the difficulties of incorporating source syntax in a string-to- tree model. We then propose a new way to use the source syntax in a fuzzy manner, both in source syntactic annotation and in rule matching. We further explore three algorithms in rule matching: 0-1 matching, likelihood matching, and deep similarity matching. Our method not only guarantees grammatical output with an explicit target tree, but also enables the system to choo...