Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs

Bryan Eikema, Germán Kruszewski, Hady Elsahar, Marc Dymetman

CtrlGen workshop Neurips 2021
[paper]

In this work, we propose a new approximate sampling technique, Quasi Rejection Sampling (QRS), that allows for a trade-off between sampling efficiency and sampling quality, while providing explicit convergence bounds and diagnostics. QRS capitalizes on the availability of high-quality global proposal distributions obtained from deep learning models. We demonstrate the effectiveness of QRS sampling for discrete EBMs over text for the tasks of controlled text generation with distributional constraints and paraphrase generation. We show that we can sample from such EBMs with arbitrary precision at the cost of sampling efficiency.