{"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/multiview-contextual-commonsense-inference-a","title":"Multiview Contextual Commonsense Inference: A New Dataset and Task","arxiv_id":"2210.02890","date":"2022-10-06","proceeding":null,"authors":["Siqi Shen","Deepanway Ghosal","Navonil Majumder","Henry Lim","Rada Mihalcea","Soujanya Poria"],"abstract":"Contextual commonsense inference is the task of generating various types of explanations around the events in a dyadic dialogue, including cause, motivation, emotional reaction, and others. Producing a coherent and non-trivial explanation requires awareness of the dialogue's structure and of how an event is grounded in the context. In this work, we create CICEROv2, a dataset consisting of 8,351 instances from 2,379 dialogues, containing multiple human-written answers for each contextual commonsense inference question, representing a type of explanation on cause, subsequent event, motivation, and emotional reaction. We show that the inferences in CICEROv2 are more semantically diverse than other contextual commonsense inference datasets. To solve the inference task, we propose a collection of pre-training objectives, including concept denoising and utterance sorting to prepare a pre-trained model for the downstream contextual commonsense inference task. Our results show that the proposed pre-training objectives are effective at adapting the pre-trained T5-Large model for the contextual commonsense inference task.","url_abs":"https://arxiv.org/abs/2210.02890v2","url_pdf":"https://arxiv.org/pdf/2210.02890v2.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":"multiview-contextual-commonsense-inference-a","repo_url":"https://github.com/declare-lab/CICERO","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multiview-contextual-commonsense-inference","task_name":"Multiview Contextual Commonsense Inference"}],"methods":[],"datasets_introduced":[{"slug":"cicerov2","name":"CICEROv2","full_name":"Contextualized Commonsense Inference in Dialogues (V2)"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiview-contextual-commonsense-inference-on-1","task":"Multiview Contextual Commonsense Inference","dataset":"CICERO","model":"DIALECT","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"27.54"},"uses_additional_data":true},{"leaderboard":"/sota/multiview-contextual-commonsense-inference-on-1","task":"Multiview Contextual Commonsense Inference","dataset":"CICERO","model":"T5-large","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"25.66"},"uses_additional_data":false},{"leaderboard":"/sota/multiview-contextual-commonsense-inference-on","task":"Multiview Contextual Commonsense Inference","dataset":"CICEROv2","model":"DIALECT","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"73.80"},"uses_additional_data":true},{"leaderboard":"/sota/multiview-contextual-commonsense-inference-on","task":"Multiview Contextual Commonsense Inference","dataset":"CICEROv2","model":"T5-large","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"71.95"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.02890","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}