CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

Understand

In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.

• Existing language generation techniques are usually based on recurrent neural networks (RNNs).
• However, it is non-trivial to impose constraints on RNNs while maintaining generation quality, since RNNs generate sentences sequentially (or with beam search) from the first word to the last.
• In this paper, we propose CGMH, a novel approach using Metropolis-Hastings sampling for constrained sentence generation.