  Abductive and counterfactual reasoning, core abilities of everyday human
cognition, require reasoning about what might have happened at time t, while
conditioning on multiple contexts from the relative past and future. However,
simultaneous incorporation of past and future contexts using generative
language models (LMs) can be challenging, as they are trained either to
condition only on the past context or to perform narrowly scoped
text-infilling. In this paper, we propose DeLorean, a new unsupervised decoding
algorithm that can flexibly incorporate both the past and future contexts using
only off-the-shelf, left-to-right language models and no supervision. The key
intuition of our algorithm is incorporating the future through
back-propagation, during which, we only update the internal representation of
the output while fixing the model parameters. By alternating between forward
and backward propagation, DeLorean can decode the output representation that
reflects both the left and right contexts. We demonstrate that our approach is
general and applicable to two nonmonotonic reasoning tasks: abductive text
generation and counterfactual story revision, where DeLorean outperforms a
range of unsupervised and some supervised methods, based on automatic and human
evaluation.
