A Dependency Based Statistical Translation Model

Giuseppe Attardi, Atanas Chanev, Antonio Valerio Miceli Barone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a translation model based on dependency trees. The model adopts a tree-to-string approach and extends Phrase-Based translation (PBT) by using the dependency tree of the source sentence for selecting translation options and for reordering them. Decoding is done by translating each node in the tree and combining its translations with those of its head in alternative orders with respect to its siblings. Reordering of the siblings exploits a heuristic based on the syntactic information from the parse tree which is learned from the corpus. The decoder uses the same phrase tables produced by a PBT system for looking up translations of single words or of partial sub-trees. A mathematical model is presented and experimental results are discussed.
Original languageEnglish
Title of host publicationProceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Pages79-87
Number of pages9
ISBN (Print)978-1-932432-99-2
Publication statusPublished - 2011

Publication series

NameSSST-5
PublisherAssociation for Computational Linguistics

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