  As transparency becomes key for robotics and AI, it will be necessary to
evaluate the methods through which transparency is provided, including
automatically generated natural language (NL) explanations. Here, we explore
parallels between the generation of such explanations and the much-studied
field of evaluation of Natural Language Generation (NLG). Specifically, we
investigate which of the NLG evaluation measures map well to explanations. We
present the ExBAN corpus: a crowd-sourced corpus of NL explanations for
Bayesian Networks. We run correlations comparing human subjective ratings with
NLG automatic measures. We find that embedding-based automatic NLG evaluation
methods, such as BERTScore and BLEURT, have a higher correlation with human
ratings, compared to word-overlap metrics, such as BLEU and ROUGE. This work
has implications for Explainable AI and transparent robotic and autonomous
systems.
