Abstract

We present the results from the 8th round of the WMT shared task on MT Automatic Post-Editing, which consists in automatically correcting the output of a ‘black-box’ machine translation system by learning from human corrections. This year, the task focused on a new language pair (English→Marathi) and on data coming from multiple domains (healthcare, tourism, and general/news). Although according to several indicators this round was of medium-high difficulty compared to the past, the best submission from the three participating teams managed to significantly improve (with an error reduction of 3.49 TER points) the original translations produced by a generic neural MT system.