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Dependency graph-to-string translation

Li, Liangyou orcid logoORCID: 0000-0002-0279-003X, Way, Andy orcid logoORCID: 0000-0001-5736-5930 and Liu, Qun orcid logoORCID: 0000-0002-7000-1792 (2015) Dependency graph-to-string translation. In: 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 17-21 Sept 2015, Lisbon, Portugal.

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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/D15-1004
Copyright Information:© 2015 Association for Computational Linguistics.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:People Programme (Marie Curie Actions) of the European Union’s Framework Programme (FP7/2007- 2013) under REA grant agreement no 317471, ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:23335
Deposited On:21 May 2019 15:44 by Thomas Murtagh . Last Modified 22 Jul 2019 14:04
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