Dependency treelet translation
2005pp. 271–279
Citations Over TimeTop 1% of 2005 papers
Abstract
We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
Related Papers
- → Exploiting Synergies Between Open Resources for German Dependency Parsing, POS-tagging, and Morphological Analysis(2013)44 cited
- → A Survey of Unsupervised Dependency Parsing(2020)9 cited
- Easy-First Chinese POS Tagging and Dependency Parsing(2012)
- → Factors influencing dependency parsing of coordinating structure(2009)1 cited
- → Concurrent Parsing of Constituency and Dependency(2019)