Results of the WMT17 Neural MT Training Task

This paper presents the results of the WMT17 Neural MT Training Task. The objective of this task is to explore the methods of training a fixed neural architecture, aiming primarily at the best translation quality and, as a secondary goal, shorter training time. Task participants were provided with a complete neural machine translation system, fixed training data and the configuration of the network. The translation was performed in the English-to-Czech direction and the task was divided into two subtasks of different configurations - one scaled to fit on a 4GB and another on an 8GB GPU card. We received 3 submissions for the 4GB variant and 1 submission for the 8GB variant; we provided also our run for each of the sizes and two baselines. We translated the test set with the trained models and evaluated the outputs using several automatic metrics. We also report results of the human evaluation of the submitted systems

Findings of the 2017 Conference on Machine Translation

This paper presents the results of the WMT17 shared tasks, which included three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task

Findings of the 2017 Conference on Machine Translation (WMT17)

This paper presents the results of theWMT17 shared tasks, which included three machine translation (MT) tasks(news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task

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

Findings of the 2018 Conference on Machine Translation (WMT18)

This paper presents the results of the premier shared task organized alongside the Confer- ence on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test suites to probe specific aspects of transla- tion

Extracting correctly aligned segments from unclean parallel data using character n-gram matching

Training of Neural Machine Translation systems is a time- and resource-demanding task, especially when large amounts of parallel texts are used. In addition, it is sensitive to unclean parallel data. In this work, we explore a data cleaning method based on character n-gram matching. The method is particularly convenient for closely related language since the n-gram matching scores can be calculated directly on the source and the target parts of the training corpus. For more distant languages, a translation step is needed and then the MT output is compared with the corresponding original part. We show that the proposed method not only reduces the amount of training corpus, but also can increase the system’s performance

ParaCrawl: Web-Scale Acquisition of Parallel Corpora

We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems