Papers › CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues

25 Mar 2022ACL 2022 5arXiv:2203.13926archive 2025-07-28

Deepanway Ghosal, Siqi Shen, Navonil Majumder, Rada Mihalcea, Soujanya Poria

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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Tasks

Answer GenerationAnswer Selection

Datasets

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CICERO

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Answer Generation CICERO T5-large pre-trained on GLUCOSE ROUGE 0.2980 #1 of 2 Archive leaderboard report
Answer Generation CICERO T5-large ROUGE 0.2947 #2 of 2 Archive leaderboard report
Answer Selection CICERO T5-large Exact Match 77.68 #1 of 2 Archive leaderboard report
Answer Selection CICERO Unified QA Exact Match 77.51 #2 of 2 Archive leaderboard report
Generative Question Answering CICERO T5-large pre-trained on GLUCOSE ROUGE 0.2980 #1 of 4 Archive leaderboard report
Generative Question Answering CICERO T5-large ROUGE 0.2946 #2 of 4 Archive leaderboard report
Generative Question Answering CICERO T5-large pre-trained on COMET ROUGE 0.2878 #3 of 4 Archive leaderboard report
Generative Question Answering CICERO BART ROUGE 0.2837 #4 of 4 Archive leaderboard report

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