  Providing natural language explanations for recommendations is particularly
useful from the perspective of a non-expert user. Although several methods for
providing such explanations have recently been proposed, we argue that an
important aspect of explanation quality has been overlooked in their
experimental evaluation. Specifically, the coherence between generated text and
predicted rating, which is a necessary condition for an explanation to be
useful, is not properly captured by currently used evaluation measures. In this
paper, we highlight the issue of explanation and prediction coherence by 1)
presenting results from a manual verification of explanations generated by one
of the state-of-the-art approaches 2) proposing a method of automatic coherence
evaluation 3) introducing a new transformer-based method that aims to produce
more coherent explanations than the state-of-the-art approaches 4) performing
an experimental evaluation which demonstrates that this method significantly
improves the explanation coherence without affecting the other aspects of
recommendation performance.
