  Abductive reasoning aims to find plausible explanations for an event. This
style of reasoning is critical for commonsense tasks where there are often
multiple plausible explanations. Existing approaches for abductive reasoning in
natural language processing (NLP) often rely on manually generated annotations
for supervision; however, such annotations can be subjective and biased.
Instead of using direct supervision, this work proposes an approach for
abductive commonsense reasoning that exploits the fact that only a subset of
explanations is correct for a given context. The method uses posterior
regularization to enforce a mutual exclusion constraint, encouraging the model
to learn the distinction between fluent explanations and plausible ones. We
evaluate our approach on a diverse set of abductive reasoning datasets;
experimental results show that our approach outperforms or is comparable to
directly applying pretrained language models in a zero-shot manner and other
knowledge-augmented zero-shot methods.
