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Improving machine translation of English relative clauses with automatic text simplification

Štajner, Sanja and Popović, Maja ORCID: 0000-0001-8234-8745 (2018) Improving machine translation of English relative clauses with automatic text simplification. In: INLG 1st Workshop on Automatic Text Adaptation (ATA 18), 5-8 Nov 2018, Tilburg, Netherlands.

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Abstract

This article explores the use of automatic sentence simplification as a preprocessing step in neural machine translation of English relative clauses into grammatically complex languages. Our experiments on English-to-Serbian and English to-German translation show that this approach can reduce technical post-editing effort (number of post-edit operations) to obtain correct translation. We find that larger improvements can be achieved for more complex target languages, as well as for MT systems with lower overall performance. The improvements mainly originate from correctly simplified sentences with relatively complex structure, while simpler structures are already translated sufficiently well using the original source sentences.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:English-to-Serbian; English-to-German
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: Jönsson, Arne and Rennes, Evelina, (eds.) Proceedings of the 1st Workshop on Automatic Text Adaptation (ATA). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://www.aclweb.org/anthology/W18-7006
Copyright Information:© 2018 Association for Computational Linguistics (ACL)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:This research was supported by the ADAPT Centre for Digital Content Technology at Dublin City University, funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and co- funded under the European Regional Development Fund
ID Code:23200
Deposited On:24 Apr 2019 11:52 by Thomas Murtagh . Last Modified 25 May 2020 16:02

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