  Publicly available, large pretrained LanguageModels (LMs) generate text with
remarkable quality, but only sequentially from left to right. As a result, they
are not immediately applicable to generation tasks that break the
unidirectional assumption, such as paraphrasing or text-infilling,
necessitating task-specific supervision.
  In this paper, we present Reflective Decoding, a novel unsupervised algorithm
that allows for direct application of unidirectional LMs to non-sequential
tasks. Our 2-step approach requires no supervision or even parallel corpora,
only two off-the-shelf pretrained LMs in opposite directions: forward and
backward. First, in the contextualization step, we use LMs to generate
ensembles of past and future contexts which collectively capture the input
(e.g. the source sentence for paraphrasing). Second, in the reflection step, we
condition on these "context ensembles", generating outputs that are compatible
with them. Comprehensive empirical results demonstrate that Reflective Decoding
outperforms strong unsupervised baselines on both paraphrasing and abductive
text infilling, significantly narrowing the gap between unsupervised and
supervised methods. Reflective Decoding surpasses multiple supervised baselines
on various metrics including human evaluation.
