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Graph-based translation via graph segmentation

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 (2016) Graph-based translation via graph segmentation. In: 54th Annual Meeting of the Association for Computational Linguistics, 7-12 Aug 2016, Berlin, Germany.

Abstract
One major drawback of phrase-based translation is that it segments an input sentence into continuous phrases. To support linguistically informed source discontinuity, in this paper we construct graphs which combine bigram and dependency relations and propose a graph-based translation model. The model segments an input graph into connected subgraphs, each of which may cover a discontinuous phrase. We use beam search to combine translations of each subgraph left-to-right to produce a complete translation. Experiments on Chinese–English and German– English tasks show that our system is significantly better than the phrase-based model by up to +1.5/+0.5 BLEU scores. By explicitly modeling the graph segmentation, our system obtains further improvement, especially on German–English.
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: Erk, Katrin and Smith, Noah A., (eds.) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Long Papers). 1. Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/P16-1010
Copyright Information:© 2016 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, European Union’s Horizon 2020 research and innovation programme under grant agreement n o 645452 (QT21)., 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:23346
Deposited On:22 May 2019 14:01 by Thomas Murtagh . Last Modified 22 May 2019 14:01
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