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A Novel approach to dropped pronoun translation

Wang, Longyue ORCID: 0000-0002-9062-6183, Tu, Zhaopeng, Zhang, Xiaojun ORCID: 0000-0003-3514-1981, Li, Hang, Way, Andy ORCID: 0000-0001-5736-5930 and Liu, Qun ORCID: 0000-0002-7000-1792 (2016) A Novel approach to dropped pronoun translation. In: 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2016), 12-17 June 2016, San Diego, CA, USA.

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Abstract

Dropped Pronouns (DP) in which pronouns are frequently dropped in the source language but should be retained in the target language are challenge in machine translation. In response to this problem, we propose a semisupervised approach to recall possibly missing pronouns in the translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation is two-phase: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. Experimental results show that our approach achieves a significant improvement of 1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy.

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 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/N16-1113
Copyright Information:© 2016 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation of Ireland (SFI) ADAPT project (Grant No.:13/RC/2106), DCU-Huawei Joint Project (Grant No.:201504032- A (DCU), YB2015090061 (Huawei)
ID Code:23215
Deposited On:01 May 2019 14:53 by Thomas Murtagh . Last Modified 01 May 2019 14:53

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