Matthias Huck

TL;DR: The results of the WMT16 shared tasks are presented, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task.

TL;DR: This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019, asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories.

TL;DR: 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 are presented.

TL;DR: The WMT15 shared task as discussed by the authors included a standard news translation task, a metrics task, tuning task, and a task for run-time estimation of machine translation quality, and an automatic post-editing task.

TL;DR: A novel reordering model for the hierarchical phrase-based approach is introduced which further enhances translation performance, and the effect some recent extended lexicon models have on the performance of the system is analyzed.