  Generating plausible and fluent sentence with desired properties has long
been a challenge. Most of the recent works use recurrent neural networks (RNNs)
and their variants to predict following words given previous sequence and
target label. In this paper, we propose a novel framework to generate
constrained sentences via Gibbs Sampling. The candidate sentences are revised
and updated iteratively, with sampled new words replacing old ones. Our
experiments show the effectiveness of the proposed method to generate plausible
and diverse sentences.
