The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018

Abstract

This paper describes the statistical machine translation systems developed at RWTH Aachen University for the GermanEnglish, EnglishTurkish and ChineseEnglish translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the GermanEnglish task where we scored first with respect to all automatic metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8% BLEU over our last year's submission and by 4.8% BLEU over the winning system of the 2017 GermanEnglish task. In EnglishTurkish task, we show 3.6% BLEU improvement over the last year's winning system. We further report results on the ChineseEnglish task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems.