CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues
This work addresses the need for dialogue-centric commonsense knowledge datasets to improve reasoning in conversational AI, representing an incremental contribution by providing a new resource for the field.
The paper tackles the problem of dialogue reasoning by curating CICERO, a dataset of 53,105 contextualized commonsense inferences from 5,672 dialogues, and uses it to solve generative and discriminative tasks, with results confirming the dataset's value for advancing research in this area.
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.