The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16
Table 1: English-German lower-cased BLEU scores calculated with Moses mteval-v13a.pl.
['[BOLD] Setup', '[EMPTY]', '[BOLD] news-test2014', '[BOLD] news-test2015', '[BOLD] news-test2016']
Tab. test2014, news-test2015, and news-test2016 For SGNMT, we see further improvements of between +0.7 BLEU (news-test2014) and +1.1 BLEU (news-test2015) by using NMT ensembles rather than single NMT. However, these gains are rather small considering the improvements from using ensembles for the (pure) NMT baseline (between +1.9 BLEU and +2.2 BLEU). Our combination scheme makes better use of the ensembles. Back-translation is a technique for making use of monolingual data in NMT training, and we expect our system could benefit from back-translation, although we leave this analysis to future work. The combination procedure we propose is non-trivial. It is not immediately clear how the gains arise as the final scores are mixtures between edit distance costs, NMT scores, and Hiero scores. In the remainder we will try to provide some insight. Unless stated otherwise, we report investigations into the Hiero + NMT 8-system ensemble which yields the best results in Tab.