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Extending feature decay algorithms using alignment entropy

Poncelas, Alberto ORCID: 0000-0002-5089-1687, Toral, Antonio ORCID: 0000-0003-2357-2960 and Way, Andy ORCID: 0000-0001-5736-5930 (2017) Extending feature decay algorithms using alignment entropy. In: FETLT 2016: Future and Emerging TrenFETLT 2016: Future and Emerging Trends in Language Technologies, Machine Learning and Big Datauage Technologies, Machine Learning and Big Data. 2nd International Workshop, 30 Nov- 2 Dec 2016, Seville, Spain.

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Abstract

In machine-learning applications, data selection is of crucial importance if good runtime performance is to be achieved. Feature Decay Algorithms (FDA) have demonstrated excellent performance in a number of tasks. While the decay function is at the heart of the success of FDA, its parameters are initialised with the same weights. In this paper, we investigate the effect on Machine Translation of assigning more appropriate weights to words using word-alignment entropy. In experiments on German to English, we show the effect of calculating these weights using two popular alignment methods, GIZA++ and FastAlign, using both automatic and human evaluations. We demonstrate that our novel FDA model is a promising research direction.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Data selection; Machine translation; Mathematical foundations
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 FETLT 2016: Future and Emerging Trends in Language Technologies, Machine Learning and Big Data. Lecture Notes in Computer Science 10341. Springer.
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/978-3-319-69365-1_14
Copyright Information:© 2016 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106), European Regional Development Fund, and the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (AbuMaTran)
ID Code:23232
Deposited On:02 May 2019 12:00 by Thomas Murtagh . Last Modified 22 Jan 2021 14:17

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