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Dependency treelet translation: Syntactically informed phrasal SMT (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Menezes, Arul Quirk, Chris Cherry, Colin |
| 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 treebased 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. 1. |
| File Format | |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Tree-based Ordering Model Unsupervised Word Alignment Component Novel Approach Promising Approach Source Dependency Parse Parallel Corpus Statistical Machine Translation Linguistic Generality Efficient Decoder Source Language Extract Dependency Treelet Translation Pair Source-language Dependency Parser Conventional Smt Model Target Sentence Recent Advance Phrasal Smt Dependency Treelet Translation Syntactic Information Target Language Word Segmentation Phrasal Translation |
| Content Type | Text |
