Abstract
In this paper, we propose and evaluate a novel dynamic feature function for log-linear model combinations in phrase-based statistical machine translation. The feature function is inspired on the popularly known vector-space model which is typically used in information retrieval and text mining applications, and it aims at improving translation unit selection at decoding time by incorporating context information from the source language. Significant improvements on an English-Spanish experimental corpus are presented and discussed.
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Notes
But it still falls within the range of acceptability based on the fact that other corpora of similar size are used. See for instance IWSLT International Evaluation Campaign (http://mastarpj.nict.go.jp/IWSLT2009/.
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Acknowledgements
The authors would like to thank Barcelona Media Innovation Center and Institute for Infocomm Research for its support and permission to publish this research. We would also like to thank Bart Mellebeek for his helpful contribution. We would like to give credit to the anonymous reviewers of this paper for their valuable suggestions.
This work has been partially funded by the Spanish Department of Education and Science through the Juan de la Cierva fellowship program and the Spanish Government under the BUCEADOR project (TEC2009-14094-C04-01).
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Costa-jussà, M.R., Banchs, R.E. A vector-space dynamic feature for phrase-based statistical machine translation. J Intell Inf Syst 37, 139–154 (2011). https://doi.org/10.1007/s10844-010-0130-7
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DOI: https://doi.org/10.1007/s10844-010-0130-7