  This paper studies constrained text generation, which is to generate
sentences under certain pre-conditions. We focus on CommonGen, the task of
generating text based on a set of concepts, as a representative task of
constrained text generation. Traditional methods mainly rely on supervised
training to maximize the likelihood of target sentences.However, global
constraints such as common sense and coverage cannot be incorporated into the
likelihood objective of the autoregressive decoding process. In this paper, we
consider using reinforcement learning to address the limitation, measuring
global constraints including fluency, common sense and concept coverage with a
comprehensive score, which serves as the reward for reinforcement learning.
Besides, we design a guided decoding method at the word, fragment and sentence
levels. Experiments demonstrate that our method significantly increases the
concept coverage and outperforms existing models in various automatic
evaluations.
