CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
Deepanway Ghosal, Siqi Shen, Navonil Majumder, Rada F. Mihalcea, Soujanya Poria · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 CI-CERO will open new research avenues into commonsense-based dialogue reasoning.