  An emerging line of research in Explainable NLP is the creation of datasets
enriched with human-annotated explanations and rationales, used to build and
evaluate models with step-wise inference and explanation generation
capabilities. While human-annotated explanations are used as ground-truth for
the inference, there is a lack of systematic assessment of their consistency
and rigour. In an attempt to provide a critical quality assessment of
Explanation Gold Standards (XGSs) for NLI, we propose a systematic annotation
methodology, named Explanation Entailment Verification (EEV), to quantify the
logical validity of human-annotated explanations. The application of EEV on
three mainstream datasets reveals the surprising conclusion that a majority of
the explanations, while appearing coherent on the surface, represent logically
invalid arguments, ranging from being incomplete to containing clearly
identifiable logical errors. This conclusion confirms that the inferential
properties of explanations are still poorly formalised and understood, and that
additional work on this line of research is necessary to improve the way
Explanation Gold Standards are constructed.
