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Improved feature decay algorithms for statistical machine translation

Published online by Cambridge University Press:  22 September 2020

Alberto Poncelas Open the ORCID record for Alberto Poncelas [Opens in a new window] ,
Gideon Maillette de Buy Wenniger Open the ORCID record for Gideon Maillette de Buy Wenniger [Opens in a new window]  and
Andy Way Open the ORCID record for Andy Way [Opens in a new window]
Alberto Poncelas*
Affiliation:
ADAPT Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
Gideon Maillette de Buy Wenniger
Affiliation:
ADAPT Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
Andy Way
Affiliation:
ADAPT Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
*
*Corresponding author. E-mail: [email protected]
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Abstract

In machine-learning applications, data selection is of crucial importance if good runtime performance is to be achieved. In a scenario where the test set is accessible when the model is being built, training instances can be selected so they are the most relevant for the test set. Feature Decay Algorithms (FDA) are a technique for data selection that has demonstrated excellent performance in a number of tasks. This method maximizes the diversity of the n-grams in the training set by devaluing those ones that have already been included. We focus on this method to undertake deeper research on how to select better training data instances. We give an overview of FDA and propose improvements in terms of speed and quality. Using German-to-English parallel data, first we create a novel approach that decreases the execution time of FDA when multiple computation units are available. In addition, we obtain improvements on translation quality by extending FDA using information from the parallel corpus that is generally ignored.

Keywords

Machine translationData selectionStatistical methods
Type
Article
Information
Natural Language Engineering , Volume 28 , Issue 1 , January 2022 , pp. 71 - 91
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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