In the last year, as an industry we have read a lot about the superiority of neural machine translation approaches over “traditional” phrase-based statistical and rule-based approaches. These claims are often based on shallow automatic comparisons or A-B testing with users.
In this webinar, we present a detailed investigation into the errors produced by different approaches, both in the general and in the technical domain. From this, we draw conclusions about what needs to be taken care of while using the one or other type of MT.
This webinar is part of the Research and Innovation Action "Quality Translation 21 (QT21)." This project has received funding from the European Union's Horizon 2020 program for ICT under grant agreement no. 645452.
Aljoscha is lab manager at the Language Technology Lab of the German Research Center for Artificial Intelligence (DFKI GmbH). His interests include the evaluation of (machine) translation quality and the inclusion of language professionals in the MT R&D workflow. Burchardt is co-developer of the MQM framework for measuring translation quality. He has a background in semantic Language Technology.