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Extending Feature Decay Algorithms Using Alignment Entropy

  • Alberto PoncelasEmail author
  • Andy Way
  • Antonio Toral
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10341)

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.

Keywords

Data selection Machine translation Mathematical foundations 

Notes

Acknowledgements

This research is supported by the ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded under the European Regional Development Fund, and the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (AbuMaTran).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ADAPT Centre, School of ComputingDublin City UniversityDublinIreland

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