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Robust sub-sentential alignment of phrase-structure trees

Groves, Declan and Hearne, Mary and Way, Andy (2004) Robust sub-sentential alignment of phrase-structure trees. In: COLING 2004 - 20th International Conference on Computational Linguistics, 23-27 August 2004, Geneva, Switzerland.

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Data-Oriented Translation (DOT), based on Data-Oriented Parsing (DOP), is a language-independent MT engine which exploits parsed, aligned bitexts to produce very high quality translations. However, data acquisition constitutes a serious bottleneck as DOT requires parsed sentences aligned at both sentential and sub-structural levels. Manual substructural alignment is time-consuming, error-prone and requires considerable knowledge of both source and target languages and how they are related. Automating this process is essential in order to carry out the large-scale translation experiments necessary to assess the full potential of DOT. We present a novel algorithm which automatically induces sub-structural alignments between context-free phrase structure trees in a fast and consistent fashion requiring little or no knowledge of the language pair. We present results from a number of experiments which indicate that our method provides a serious alternative to manual alignment.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:data-oriented translation (DOT);
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Association for Computational Linguistics
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science Engineering and Technology
ID Code:15307
Deposited On:15 Mar 2010 14:24 by DORAS Administrator. Last Modified 20 Feb 2017 13:48

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