  Generating long and informative review text is a challenging natural language
generation task. Previous work focuses on word-level generation, neglecting the
importance of topical and syntactic characteristics from natural languages. In
this paper, we propose a novel review generation model by characterizing an
elaborately designed aspect-aware coarse-to-fine generation process. First, we
model the aspect transitions to capture the overall content flow. Then, to
generate a sentence, an aspect-aware sketch will be predicted using an
aspect-aware decoder. Finally, another decoder fills in the semantic slots by
generating corresponding words. Our approach is able to jointly utilize aspect
semantics, syntactic sketch, and context information. Extensive experiments
results have demonstrated the effectiveness of the proposed model.
