  Enthymemes are defined as arguments where a premise or conclusion is left
implicit. We tackle the task of generating the implicit premise in an
enthymeme, which requires not only an understanding of the stated conclusion
and premise but also additional inferences that could depend on commonsense
knowledge. The largest available dataset for enthymemes (Habernal et al., 2018)
consists of 1.7k samples, which is not large enough to train a neural text
generation model. To address this issue, we take advantage of a similar task
and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020).
However, we show that simply using a state-of-the-art seq2seq model fine-tuned
on this data might not generate meaningful implicit premises associated with
the given enthymemes. We demonstrate that encoding discourse-aware commonsense
during fine-tuning improves the quality of the generated implicit premises and
outperforms all other baselines both in automatic and human evaluations on
three different datasets.
