Type

Conference Proceedings

Authors

Qun Liu
Andy Way
Liangyou Li

Subjects

Linguistics

Topics
english hierarchical model statistical machine translation large scale state of the art dependency translation machine translating

Dependency graph-to-string translation (2015)

Abstract Compared to tree grammars, graph grammars have stronger generative capacity over structures. Based on an edge replacement grammar, in this paper we propose to use a synchronous graph-to-string grammar for statistical machine translation. The graph we use is directly converted from a dependency tree by labelling edges. We build our translation model in the log-linear framework with standard features. Large-scale experiments on Chinese–English and German–English tasks show that our model is significantly better than the state-of-the-art hierarchical phrase-based (HPB) model and a recently improved dependency tree-to-string model on BLEU, METEOR and TER scores. Experiments also suggest that our model has better capability to perform long-distance reordering and is more suitable for translating long sentences.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Qun Liu, Andy Way, Liangyou Li

Experts in our system

1
Qun Liu
Dublin City University
Total Publications: 31
 
2
Andy Way
Dublin City University
Total Publications: 229
 
3
Liangyou Li
Dublin City University
Total Publications: 9