This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).
Microsoft, Charles University, Toloka, NTT Resonant, Lingua Custodia, Webinterpret, Google, Cyber Agent, Phrase, European Commission via its H2020 Program (project WELCOME, contract no. 870930) and by 20-16819X (LUSyD), Science Foundation Ireland through the SFI Research Centres Programme and co-funded under the European Regional Development Fund (ERDF) through Grant 13/RC/2106., LM2018101 (LINDAT/CLARIAHCZ) of the Ministry of Education, Youth, and Sports of the Czech Republic
ID Code:
28361
Deposited On:
24 May 2023 11:33 by
Maja Popovic
. Last Modified 24 May 2023 11:34