The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16

Conference Object
Change log
Hasler, Eva 
Byrne, Bill 

This paper presents the University of Cambridge submission to WMT16. Motivated by the complementary nature of syntactical machine translation and neural machine translation (NMT), we exploit the synergies of Hiero and NMT in different combination schemes. Starting out with a simple neural lattice rescoring approach, we show that the Hiero lattices are often too narrow for NMT ensembles. Therefore, instead of a hard restriction of the NMT search space to the lattice, we propose to loosely couple NMT and Hiero by composition with a modified version of the edit distance transducer. The loose combination outperforms lattice rescoring, especially when using multiple NMT systems in an ensemble.

cs.CL, cs.CL
Journal Title
ACL 2016 First Conference On Machine Translation (WMT16)
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Journal ISSN
Volume Title
Association for Computational Linguistics
Engineering and Physical Sciences Research Council (EP/L027623/1)