{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cicero-a-dataset-for-contextualized","title":"CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues","arxiv_id":"2203.13926","date":"2022-03-25","proceeding":"ACL 2022 5","authors":["Deepanway Ghosal","Siqi Shen","Navonil Majumder","Rada Mihalcea","Soujanya Poria"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2203.13926v3","url_pdf":"https://arxiv.org/pdf/2203.13926v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cicero-a-dataset-for-contextualized","repo_url":"https://github.com/declare-lab/CICERO","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"answer-generation","task_name":"Answer Generation"},{"task_slug":"answer-selection","task_name":"Answer Selection"}],"methods":[],"datasets_introduced":[{"slug":"cicero","name":"CICERO","full_name":"Contextualized Commonsense Inference in Dialogues"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/answer-generation-on-cicero","task":"Answer Generation","dataset":"CICERO","model":"T5-large pre-trained on GLUCOSE","rank_in_archive_order":1,"of":2,"metrics":{"ROUGE":"0.2980"},"uses_additional_data":false},{"leaderboard":"/sota/answer-generation-on-cicero","task":"Answer Generation","dataset":"CICERO","model":"T5-large","rank_in_archive_order":2,"of":2,"metrics":{"ROUGE":"0.2947"},"uses_additional_data":false},{"leaderboard":"/sota/answer-selection-on-cicero","task":"Answer Selection","dataset":"CICERO","model":"T5-large","rank_in_archive_order":1,"of":2,"metrics":{"Exact Match":"77.68"},"uses_additional_data":false},{"leaderboard":"/sota/answer-selection-on-cicero","task":"Answer Selection","dataset":"CICERO","model":"Unified QA","rank_in_archive_order":2,"of":2,"metrics":{"Exact Match":"77.51"},"uses_additional_data":false},{"leaderboard":"/sota/generative-question-answering-on-cicero","task":"Generative Question Answering","dataset":"CICERO","model":"T5-large pre-trained on GLUCOSE","rank_in_archive_order":1,"of":4,"metrics":{"ROUGE":"0.2980"},"uses_additional_data":false},{"leaderboard":"/sota/generative-question-answering-on-cicero","task":"Generative Question Answering","dataset":"CICERO","model":"T5-large","rank_in_archive_order":2,"of":4,"metrics":{"ROUGE":"0.2946"},"uses_additional_data":false},{"leaderboard":"/sota/generative-question-answering-on-cicero","task":"Generative Question Answering","dataset":"CICERO","model":"T5-large pre-trained on COMET","rank_in_archive_order":3,"of":4,"metrics":{"ROUGE":"0.2878"},"uses_additional_data":false},{"leaderboard":"/sota/generative-question-answering-on-cicero","task":"Generative Question Answering","dataset":"CICERO","model":"BART","rank_in_archive_order":4,"of":4,"metrics":{"ROUGE":"0.2837"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.13926","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}