Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method

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.

Authors

• Jinyue Su
• Jiacheng Xu National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China
• Xipeng Qiu School of Computer Science, Fudan University
• Xuanjing Huang School of Computer Science, Fudan University, Shanghai, China Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Shanghai, China

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