1 research outputs found
The University of Sydney's Machine Translation System for WMT19
This paper describes the University of Sydney's submission of the WMT 2019
shared news translation task. We participated in the
FinnishEnglish direction and got the best BLEU(33.0) score among
all the participants. Our system is based on the self-attentional Transformer
networks, into which we integrated the most recent effective strategies from
academic research (e.g., BPE, back translation, multi-features data selection,
data augmentation, greedy model ensemble, reranking, ConMBR system combination,
and post-processing). Furthermore, we propose a novel augmentation method
and a data mixture strategy / parallel
construction to entirely exploit the synthetic corpus. Extensive experiments
show that adding the above techniques can make continuous improvements of the
BLEU scores, and the best result outperforms the baseline (Transformer ensemble
model trained with the original parallel corpus) by approximately 5.3 BLEU
score, achieving the state-of-the-art performance.Comment: To appear in WMT201