CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

by   Deepanway Ghosal, et al.
University of Michigan

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.


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Code Repositories


This repository contains the dataset and codes used in our ACL 2022 paper CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

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