{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T23:16:23Z","timestamp":1784330183863,"version":"3.55.0"},"reference-count":42,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":126,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing datasets tend to lack in-depth details, restate information already present in the conversation, and often fail to capture the multifaceted nature of commonsense reasoning. In response to these limitations, we compile a new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, \u2102onvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences. Our dataset contains over 500,000 inferences across 12,000 dialogues with 10 popular inference types, which empowers the training of generative commonsense models for dialogue that are superior in producing plausible inferences with high novelty when compared to models trained on the previous datasets. To the best of our knowledge, \u2102onvoSense is the first of its kind to provide such a multitude of novel inferences at such a large scale.<\/jats:p>","DOI":"10.1162\/tacl_a_00659","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T20:13:29Z","timestamp":1715026409000},"page":"467-483","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI"],"prefix":"10.1162","volume":"12","author":[{"given":"Sarah E.","family":"Finch","sequence":"first","affiliation":[{"name":"Department of Computer Science Emory University, Atlanta, GA, USA. sfillwo@emory.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinho D.","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Science Emory University, Atlanta, GA, USA. jinho.choi@emory.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"2024050620131635700_bib1","first-page":"563","article-title":"Algorithms for scoring coreference chains","volume-title":"Proceedings of the Linguistic Coreference Workshop at the 1st Conference on Language Resources and Evaluation","author":"Bagga","year":"1998"},{"key":"2024050620131635700_bib2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/978-1-4757-3023-4_2","article-title":"Linear assignment problems and extensions","volume-title":"Handbook of Combinatorial Optimization: Supplement volume A","author":"Burkard","year":"1999"},{"key":"2024050620131635700_bib3","doi-asserted-by":"publisher","first-page":"2411","DOI":"10.18653\/v1\/2020.findings-emnlp.218","article-title":"DivGAN: Towards diverse paraphrase generation via diversified generative adversarial network","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","author":"Cao","year":"2020"},{"key":"2024050620131635700_bib4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1037\/10096-006","article-title":"Grounding in communication","volume-title":"Perspectives on Socially Shared Cognition","author":"Clark","year":"1991"},{"key":"2024050620131635700_bib5","doi-asserted-by":"publisher","first-page":"12760","DOI":"10.1609\/aaai.v35i14.17510","article-title":"MultiTalk: A highly-branching dialog testbed for diverse conversations","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Dou","year":"2021"},{"key":"2024050620131635700_bib6","article-title":"Identification of personal information shared in chat-oriented dialogue","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)","author":"Fillwock","year":"2018"},{"key":"2024050620131635700_bib7","doi-asserted-by":"publisher","first-page":"15044","DOI":"10.18653\/v1\/2023.acl-long.839","article-title":"Don\u2019t forget your ABC\u2019s: Evaluating the state-of-the-art in chat-oriented dialogue systems","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Finch","year":"2023"},{"key":"2024050620131635700_bib8","doi-asserted-by":"publisher","first-page":"12857","DOI":"10.1609\/aaai.v35i14.17521","article-title":"Paragraph-level commonsense transformers with recurrent memory","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Gabriel","year":"2021"},{"key":"2024050620131635700_bib9","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.18653\/v1\/2022.findings-emnlp.120","article-title":"ComFact: A benchmark for linking contextual commonsense knowledge","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2022","author":"Gao","year":"2022"},{"key":"2024050620131635700_bib10","doi-asserted-by":"publisher","first-page":"301","DOI":"10.18653\/v1\/2021.sigdial-1.33","article-title":"CIDER: Commonsense inference for dialogue explanation and reasoning","volume-title":"Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue","author":"Ghosal","year":"2021"},{"key":"2024050620131635700_bib11","doi-asserted-by":"publisher","first-page":"5010","DOI":"10.18653\/v1\/2022.acl-long.344","article-title":"CICERO: A dataset for contextualized commonsense inference in dialogues","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Ghosal","year":"2022"},{"key":"2024050620131635700_bib12","first-page":"1100","article-title":"A systematic exploration of diversity in machine translation","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Gimpel","year":"2013"},{"key":"2024050620131635700_bib13","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2203.05794","article-title":"BERTopic: Neural topic modeling with a class-based TF-IDF procedure","author":"Grootendorst","year":"2022","journal-title":"arXiv preprint arXiv:2203.05794v1"},{"key":"2024050620131635700_bib14","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1162\/tacl_a_00302","article-title":"A knowledge-enhanced pretraining model for commonsense story generation","volume":"8","author":"Guan","year":"2020","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"2024050620131635700_bib15","article-title":"Kappa statistic is not satisfactory for assessing the extent of agreement between raters","volume":"1","author":"Gwet","year":"2002","journal-title":"Statistical Methods for Inter-Rater Reliability Assessment"},{"key":"2024050620131635700_bib16","doi-asserted-by":"publisher","first-page":"6384","DOI":"10.1609\/aaai.v35i7.16792","article-title":"(Comet-) Atomic 2020: On symbolic and neural commonsense knowledge graphs","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Hwang","year":"2021"},{"key":"2024050620131635700_bib17","doi-asserted-by":"publisher","first-page":"3752","DOI":"10.18653\/v1\/P19-1365","article-title":"Comparison of diverse decoding methods from conditional language models","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Ippolito","year":"2019"},{"key":"2024050620131635700_bib18","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1109\/ACII.2013.47","article-title":"Facing imbalanced data\u2013recommendations for the use of performance metrics","volume-title":"2013 Humaine association conference on affective computing and intelligent interaction","author":"Jeni","year":"2013"},{"key":"2024050620131635700_bib19","doi-asserted-by":"publisher","first-page":"605","DOI":"10.18653\/v1\/2022.sigdial-1.56","article-title":"Improving bot response contradiction detection via utterance rewriting","volume-title":"Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue","author":"Di","year":"2022"},{"key":"2024050620131635700_bib20","doi-asserted-by":"publisher","first-page":"12930","DOI":"10.18653\/v1\/2023.emnlp-main.799","article-title":"SODA: Million-scale dialogue distillation with social commonsense contextualization","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","author":"Kim","year":"2023"},{"key":"2024050620131635700_bib21","first-page":"6285","article-title":"Mind the gap! Injecting commonsense knowledge for abstractive dialogue summarization","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"Kim","year":"2022"},{"key":"2024050620131635700_bib22","doi-asserted-by":"publisher","first-page":"10993","DOI":"10.1609\/aaai.v36i10.21347","article-title":"Knowledge bridging for empathetic dialogue generation","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Li","year":"2022"},{"issue":"11","key":"2024050620131635700_bib23","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","article-title":"hdbscan: Hierarchical density based clustering","volume":"2","author":"McInnes","year":"2017","journal-title":"The Journal of Open Source Software"},{"key":"2024050620131635700_bib24","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2020","journal-title":"arXiv preprint arXiv:1802.03426v3"},{"key":"2024050620131635700_bib25","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-319-92108-2_3","article-title":"What information should a dialogue system understand?: Collection and analysis of perceived information in chat-oriented dialogue","volume-title":"Advanced Social Interaction with Agents: 8th International Workshop on Spoken Dialog Systems","author":"Mitsuda","year":"2019"},{"key":"2024050620131635700_bib26","doi-asserted-by":"publisher","first-page":"1824","DOI":"10.18653\/v1\/D19-1191","article-title":"Improving open-domain dialogue systems via multi-turn incomplete utterance restoration","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Pan","year":"2019"},{"key":"2024050620131635700_bib27","doi-asserted-by":"publisher","first-page":"311","DOI":"10.3115\/1073083.1073135","article-title":"BLEU: A method for automatic evaluation of machine translation","volume-title":"Proceedings of the 40th annual meeting of the Association for Computational Linguistics","author":"Papineni","year":"2002"},{"issue":"4","key":"2024050620131635700_bib28","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1080\/00031305.2016.1141708","article-title":"How robust are multirater interrater reliability indices to changes in frequency distribution?","volume":"70","author":"Quarfoot","year":"2016","journal-title":"The American Statistician"},{"issue":"140","key":"2024050620131635700_bib29","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"2024050620131635700_bib30","doi-asserted-by":"publisher","first-page":"3982","DOI":"10.18653\/v1\/D19-1410","article-title":"Sentence-BERT: Sentence embeddings using siamese bert-networks","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Reimers","year":"2019"},{"key":"2024050620131635700_bib31","doi-asserted-by":"publisher","first-page":"11229","DOI":"10.1609\/aaai.v36i10.21373","article-title":"CEM: Commonsense-aware empathetic response generation","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Sabour","year":"2022"},{"key":"2024050620131635700_bib32","doi-asserted-by":"publisher","first-page":"3027","DOI":"10.1609\/aaai.v33i01.33013027","article-title":"ATOMIC: An atlas of machine commonsense for if-then reasoning","volume-title":"Proceedings of the AAAI conference on artificial intelligence","author":"Sap","year":"2019"},{"key":"2024050620131635700_bib33","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2210.02890","article-title":"Multiview contextual commonsense inference: A new dataset and task","author":"Shen","year":"2022","journal-title":"arXiv preprint arXiv:2210.02890v2"},{"key":"2024050620131635700_bib34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11164","article-title":"ConceptNet 5.5: An open multilingual graph of general knowledge","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Speer","year":"2017"},{"key":"2024050620131635700_bib35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12340","article-title":"Diverse beam search: Decoding diverse solutions from neural sequence models","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Vijayakumar","year":"2018"},{"key":"2024050620131635700_bib36","doi-asserted-by":"publisher","first-page":"4602","DOI":"10.18653\/v1\/2022.naacl-main.341","article-title":"Symbolic knowledge distillation: From general language models to commonsense models","volume-title":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"West","year":"2022"},{"key":"2024050620131635700_bib37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2288-13-61","article-title":"A comparison of Cohen\u2019s Kappa and Gwet\u2019s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples","volume":"13","author":"Wongpakaran","year":"2013","journal-title":"BMC Medical Research Methodology"},{"key":"2024050620131635700_bib38","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591877","article-title":"SocialDial: A benchmark for socially-aware dialogue systems","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhan","year":"2023"},{"key":"2024050620131635700_bib39","article-title":"BERTScore: Evaluating text generation with BERT","volume-title":"International Conference on Learning Representations","author":"Zhang","year":"2019"},{"key":"2024050620131635700_bib40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.714","article-title":"Reflect not reflex: Inference-based common ground improves dialogue response quality","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing","author":"Zhou","year":"2022"},{"key":"2024050620131635700_bib41","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.18653\/v1\/2022.acl-long.88","article-title":"Think before you speak: Explicitly generating implicit commonsense knowledge for response generation","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Zhou","year":"2022"},{"key":"2024050620131635700_bib42","doi-asserted-by":"publisher","first-page":"4132","DOI":"10.18653\/v1\/2021.findings-emnlp.349","article-title":"Probing commonsense explanation in dialogue response generation","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2021","author":"Zhou","year":"2021"}],"container-title":["Transactions of the Association for Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00659\/2369521\/tacl_a_00659.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00659\/2369521\/tacl_a_00659.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T20:13:41Z","timestamp":1715026421000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/tacl\/article\/doi\/10.1162\/tacl_a_00659\/120913\/ConvoSense-Overcoming-Monotonous-Commonsense"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1162\/tacl_a_00659","relation":{},"ISSN":["2307-387X"],"issn-type":[{"value":"2307-387X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024]]},"published":{"date-parts":[[2024]]}}}