Findings of the 2019 Conference on Machine Translation (WMT19)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation

Combined Spoken Language Translation

EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system

Phrase-level System Combination for Machine Translation Based on Target-to-Target Decoding

In this paper, we propose a novel lattice-based MT combination methodology that we call Target-to-Target Decoding (TTD). The combination process is carried out as a “translation” from backbone to the combination result. This perspective suggests the use of existing phrase-based MT techniques in the combination framework. We show how phrase extraction rules and confidence estimations inspired from machine translation improve results. We also propose system-specific LMs for estimating N-gram consensus. Our results show that our approach yields a strong improvement over the best single MT system and competes with other state-of-the-art combination systems

Sentence-level quality estimation for MT system combination

This paper provides the system description of the Dublin City University system combination module for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimize the Division of Labour in Hybrid MT (ML4HMT- 12). We incorporated a sentence-level quality score, obtained by sentence-level Quality Estimation (QE), as meta information guiding system combination. Instead of using BLEU or (minimum average) TER, we select a backbone for the confusion network using the estimated quality score. For the Spanish-English data, our strategy improved 0.89 BLEU points absolute compared to the best single score and 0.20 BLEU points absolute compared to the standard system combination strateg

The QT21/HimL Combined Machine Translation System

This paper describes the joint submission of the QT21 and HimL projects for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). The submission is a system combination which combines twelve different statistical machine translation systems provided by the different groups (RWTH Aachen University, LMU Munich, Charles University in Prague, University of Edinburgh, University of Sheffield, Karlsruhe Institute of Technology, LIMSI, University of Amsterdam, Tilde). The systems are combined using RWTH’s system combination approach. The final submission shows an improvement of 1.0 BLEU compared to the best single system on newstest2016