The University of Sydney's Machine Translation System for WMT19

Abstract

This paper describes the University of Sydney's submission of the WMT 2019 shared news translation task. We participated in the Finnish$\to$English 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 $CycleTranslation$ and a data mixture strategy $Big$/$Small$ 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.

To appear in WMT2019

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Generating Long Sequences with Sparse Transformers
• Pay Less Attention with Lightweight and Dynamic Convolutions
• Weighted Transformer Network for Machine Translation