  Recent models can generate fluent and grammatical synthetic reviews while
accurately predicting user ratings. The generated reviews, expressing users'
estimated opinions towards related products, are often viewed as natural
language 'rationales' for the jointly predicted rating. However, previous
studies found that existing models often generate repetitive, universally
applicable, and generic explanations, resulting in uninformative rationales.
Further, our analysis shows that previous models' generated content often
contain factual hallucinations. These issues call for novel solutions that
could generate both informative and factually grounded explanations. Inspired
by recent success in using retrieved content in addition to parametric
knowledge for generation, we propose to augment the generator with a
personalized retriever, where the retriever's output serves as external
knowledge for enhancing the generator. Experiments on Yelp, TripAdvisor, and
Amazon Movie Reviews dataset show our model could generate explanations that
more reliably entail existing reviews, are more diverse, and are rated more
informative by human evaluators.
