  The black-box nature of neural models has motivated a line of research that
aims to generate natural language rationales to explain why a model made
certain predictions. Such rationale generation models, to date, have been
trained on dataset-specific crowdsourced rationales, but this approach is
costly and is not generalizable to new tasks and domains. In this paper, we
investigate the extent to which neural models can reason about natural language
rationales that explain model predictions, relying only on distant supervision
with no additional annotation cost for human-written rationales. We investigate
multiple ways to automatically generate rationales using pre-trained language
models, neural knowledge models, and distant supervision from related tasks,
and train generative models capable of composing explanatory rationales for
unseen instances. We demonstrate our approach on the defeasible inference task,
a nonmonotonic reasoning task in which an inference may be strengthened or
weakened when new information (an update) is introduced. Our model shows
promises at generating post-hoc rationales explaining why an inference is more
or less likely given the additional information, however, it mostly generates
trivial rationales reflecting the fundamental limitations of neural language
models. Conversely, the more realistic setup of jointly predicting the update
or its type and generating rationale is more challenging, suggesting an
important future direction.
