  Human understanding of narrative texts requires making commonsense inferences
beyond what is stated explicitly in the text. A recent model, COMET, can
generate such implicit commonsense inferences along several dimensions such as
pre- and post-conditions, motivations, and mental states of the participants.
However, COMET was trained on commonsense inferences of short phrases, and is
therefore discourse-agnostic. When presented with each sentence of a
multi-sentence narrative, it might generate inferences that are inconsistent
with the rest of the narrative.
  We present the task of discourse-aware commonsense inference. Given a
sentence within a narrative, the goal is to generate commonsense inferences
along predefined dimensions, while maintaining coherence with the rest of the
narrative. Such large-scale paragraph-level annotation is hard to get and
costly, so we use available sentence-level annotations to efficiently and
automatically construct a distantly supervised corpus.
  Using this corpus, we train PARA-COMET, a discourse-aware model that
incorporates paragraph-level information to generate coherent commonsense
inferences from narratives. PARA-COMET captures both semantic knowledge
pertaining to prior world knowledge, and episodic knowledge involving how
current events relate to prior and future events in a narrative. Our results
show that PARA-COMET outperforms the sentence-level baselines, particularly in
generating inferences that are both coherent and novel.
