Papers › The University of Sydney's Machine Translation System for WMT19

The University of Sydney's Machine Translation System for WMT19

30 Jun 2019WS 2019 8arXiv:1907.00494archive 2025-07-28

Liang Ding, DaCheng Tao

This paper describes the University of Sydney's submission of the WMT 2019 shared news translation task. We participated in the Finnish→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 Cycle Translation 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.

Paper PDF Conference PDF

Code

No code repository is listed for this paper in the archive or in Syntology's graph.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Data Augmentation Machine Translation Reranking Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Machine Translation	WMT 2018 Finnish-English	CT+B/S construction	BLEU	26.5	#1 of 2	Archive leaderboard	report
Machine Translation	WMT2016 Finnish-English	CT+B/S construction	BLEU	32.4	#1 of 1	Archive leaderboard	report
Machine Translation	WMT2017 Finnish-English	CT+B/S construction	BLEU	35.5	#1 of 1	Archive leaderboard	report
Machine Translation	WMT2019 Finnish-English	CT+B/S construction	BLEU	34.1	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections