  We propose a Distributional Approach for addressing Controlled Text
Generation from pre-trained Language Models (LMs). This approach permits to
specify, in a single formal framework, both "pointwise" and "distributional"
constraints over the target LM -- to our knowledge, the first model with such
generality -- while minimizing KL divergence from the initial LM distribution.
The optimal target distribution is then uniquely determined as an explicit EBM
(Energy-Based Model) representation. From that optimal representation we then
train a target controlled Autoregressive LM through an adaptive distributional
variant of Policy Gradient. We conduct a first set of experiments over
pointwise constraints showing the advantages of our approach over a set of
baselines, in terms of obtaining a controlled LM balancing constraint
satisfaction with divergence from the initial LM. We then perform experiments
over distributional constraints, a unique feature of our approach,
demonstrating its potential as a remedy to the problem of Bias in Language
Models. Through an ablation study, we show the effectiveness of our adaptive
technique for obtaining faster convergence. (Code available at
https://github.com/naver/gdc)
