  Long text generation is an important but challenging task.The main problem
lies in learning sentence-level semantic dependencies which traditional
generative models often suffer from. To address this problem, we propose a
Multi-hop Reasoning Generation (MRG) approach that incorporates multi-hop
reasoning over a knowledge graph to learn semantic dependencies among
sentences. MRG consists of twoparts, a graph-based multi-hop reasoning module
and a path-aware sentence realization module. The reasoning module is
responsible for searching skeleton paths from a knowledge graph to imitate the
imagination process in the human writing for semantic transfer. Based on the
inferred paths, the sentence realization module then generates a complete
sentence. Unlike previous black-box models, MRG explicitly infers the skeleton
path, which provides explanatory views tounderstand how the proposed model
works. We conduct experiments on three representative tasks, including story
generation, review generation, and product description generation. Automatic
and manual evaluation show that our proposed method can generate more
informative and coherentlong text than strong baselines, such as pre-trained
models(e.g. GPT-2) and knowledge-enhanced models.
