Findings of the 2022 Conference on Machine Translation (WMT22)
Findings of the 2022 Conference on Machine Translation (WMT22)
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发表时间：
2022
影响因子：
3
通讯作者：
Tom Kocmi;Rachel Bawden;Ondrej Bojar;Anton Dvorkovich;C. Federmann;Mark Fishel;Thamme Gowda;Yvette Graham;Roman Grundkiewicz;B. Haddow;Rebecca Knowles;Philipp Koehn;Christof Monz;Makoto Morishita;M. Nagata;Toshiaki Nakazawa;M. Novák;M. Popel;Maja Popovic
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文献类型：
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作者：
Tom Kocmi;Rachel Bawden;Ondrej Bojar;Anton Dvorkovich;C. Federmann;Mark Fishel;Thamme Gowda;Yvette Graham;Roman Grundkiewicz;B. Haddow;Rebecca Knowles;Philipp Koehn;Christof Monz;Makoto Morishita;M. Nagata;Toshiaki Nakazawa;M. Novák;M. Popel;Maja Popovic
本文介绍了作为2022年机器翻译会议(WMT)的一部分组织的通用机器翻译任务的结果。在一般机器翻译任务中，参与者被要求为11种语言对中的任何一种建立机器翻译系统，并在由四个不同领域组成的测试集上进行评估。我们利用人工注释器使用两种不同的技术来评估系统输出：基于引用的直接评估(DA)和DA和标量质量度量(DA+SQM)的组合。
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).