The University of Sydney’s Machine Translation System for WMT19
Table 6: FI→EN Results on newstest2018 and newstest2019. The submitted system is the last one.
['[BOLD] #', '[BOLD] Models', 'news-test18', 'news-test19', 'Δ [ITALIC] ave']
The Msmall configuration trained with selected parallel data improves BLEU by +0.7 points. According to exp.[3-6], adding these components can lead to continuous improvements. Notably, with Cycle Translation and Big/Small parallel construction strategy, our system could obtains +3.55 significant improvement. And exp.[8-11] show that with performing GMSE, multi-features reranking, ConMBR system combination and post-processing, our system further improved the BLEU score from 30.9 to 33.0 on the official data set news-test2019, which substantially outperforms the baseline by 5.3 BLEU score.