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.
@article{arxiv.2203.13926,
title = {CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues},
author = {Deepanway Ghosal and Siqi Shen and Navonil Majumder and Rada Mihalcea and Soujanya Poria},
journal= {arXiv preprint arXiv:2203.13926},
year = {2022}
}