  Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al.,
CoNLL 2019] for exploiting global properties of sequences for data-efficient
learning of seq2seq models. In the first phase of training, an Energy-Based
model (EBM) over sequences is derived. This EBM has high representational
power, but is unnormalized and cannot be directly exploited for sampling. To
address this issue [Parshakova et al., CoNLL 2019] proposes a distillation
technique, which can only be applied under limited conditions. By relating this
problem to Policy Gradient techniques in RL, but in a \emph{distributional}
rather than \emph{optimization} perspective, we propose a general approach
applicable to any sequential EBM. Its effectiveness is illustrated on GAM-based
experiments.
