  The advent and fast development of neural networks have revolutionized the
research on dialogue systems and subsequently have triggered various challenges
regarding their automatic evaluation. Automatic evaluation of open-domain
dialogue systems as an open challenge has been the center of the attention of
many researchers. Despite the consistent efforts to improve automatic metrics'
correlations with human evaluation, there have been very few attempts to assess
their robustness over multiple domains and dimensions. Also, their focus is
mainly on the English language. All of these challenges prompt the development
of automatic evaluation metrics that are reliable in various domains,
dimensions, and languages. This track in the 11th Dialogue System Technology
Challenge (DSTC11) is part of the ongoing effort to promote robust and
multilingual automatic evaluation metrics. This article describes the datasets
and baselines provided to participants and discusses the submission and result
details of the two proposed subtasks.
