  Personalized natural language generation for explainable recommendations
plays a key role in justifying why a recommendation might match a user's
interests. Existing models usually control the generation process by aspect
planning. While promising, these aspect-planning methods struggle to generate
specific information correctly, which prevents generated explanations from
being convincing. In this paper, we claim that introducing lexical constraints
can alleviate the above issues. We propose a model, UCEpic, that generates
high-quality personalized explanations for recommendation results by unifying
aspect planning and lexical constraints in an insertion-based generation
manner.
  Methodologically, to ensure text generation quality and robustness to various
lexical constraints, we pre-train a non-personalized text generator via our
proposed robust insertion process. Then, to obtain personalized explanations
under this framework of insertion-based generation, we design a method of
incorporating aspect planning and personalized references into the insertion
process. Hence, UCEpic unifies aspect planning and lexical constraints into one
framework and generates explanations for recommendations under different
settings. Compared to previous recommendation explanation generators controlled
by only aspects, UCEpic incorporates specific information from keyphrases and
then largely improves the diversity and informativeness of generated
explanations for recommendations on datasets such as RateBeer and Yelp.
