Principled Gradient-based Markov Chain Monte Carlo for Text Generation
Li Du Afra Amini Lucas Torroba Hennigen Xinyan Velocity Yu Jason Eisner Holden Lee Ryan Cotterell
Recent papers have demonstrated the possibility of energy-based text
generation by adapting gradient-based sampling algorithms, a paradigm of MCMC
algorithms that promises fast convergence. However, as we show in this paper,
previous attempts on this approach to text generation all fail to sample
correctly from the target language model distributions. To address this
limitation, we consider the problem of designing text samplers that are
faithful, meaning that they have the target text distribution as its limiting
distribution. We propose several faithful gradient-based sampling algorithms to
sample from the target energy-based text distribution correctly, and study
their theoretical properties. Through experiments on various forms of text
generation, we demonstrate that faithful samplers are able to generate more
fluent text while adhering to the control objectives better.