Conference Proceedings


Andy Way
Antonio Toral
Alberto Poncelas


Computer Science

alignment word alignment machine translation mathematical foundations data selection feature decay algorithms machine learning machine translating

Extending feature decay algorithms using alignment entropy (2017)

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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Andy Way, Antonio Toral, Alberto Poncelas

Experts in our system

Andy Way
Dublin City University
Total Publications: 229
Antonio Toral
Dublin City University
Total Publications: 18
Alberto Poncelas
Dublin City University
Total Publications: 8