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Context-aware graph segmentation for graph-based 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 (2017) Context-aware graph segmentation for graph-based translation. In: 15th Conference of the European Chapter of the Association for Computational Linguistics, 3-7 Apr 2017, Valencia, Spain.

Abstract
In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations leftto-right using beam search. Experiments on Chinese–English and German–English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model.
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 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:https://www.aclweb.org/anthology/E17-2095
Copyright Information:© 2017 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Union’s Horizon 2020 research and innovation programme under grant agreement no 645452 (QT21)., ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is cofunded under the European Regional Development Fund
ID Code:23332
Deposited On:21 May 2019 15:44 by Thomas Murtagh . Last Modified 21 May 2019 15:44
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