  This paper addresses the problem of dialogue reasoning with contextualized
commonsense inference. We curate CICERO, a dataset of dyadic conversations with
five types of utterance-level reasoning-based inferences: cause, subsequent
event, prerequisite, motivation, and emotional reaction. The dataset contains
53,105 of such inferences from 5,672 dialogues. We use this dataset to solve
relevant generative and discriminative tasks: generation of cause and
subsequent event; generation of prerequisite, motivation, and listener's
emotional reaction; and selection of plausible alternatives. Our results
ascertain the value of such dialogue-centric commonsense knowledge datasets. It
is our hope that CICERO will open new research avenues into commonsense-based
dialogue reasoning.
