{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:23:42Z","timestamp":1743042222462,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031446924"},{"type":"electronic","value":"9783031446931"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44693-1_52","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T08:02:39Z","timestamp":1696665759000},"page":"669-681","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["What Events Do Pre-trained Language Models Learn from\u00a0Text? Probing Event-Based Commonsense Knowledge by\u00a0Confidence Sorting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4486-9975","authenticated-orcid":false,"given":"Jiachun","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2248-2966","authenticated-orcid":false,"given":"Chenhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5485-9916","authenticated-orcid":false,"given":"Yubo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6083-8433","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-2263","authenticated-orcid":false,"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Apidianaki, M., Soler, A.G.: ALL dolphins are intelligent and SOME are friendly: probing BERT for nouns\u2019 semantic properties and their prototypicality. CoRR abs\/2110.06376 (2021)","key":"52_CR1","DOI":"10.18653\/v1\/2021.blackboxnlp-1.7"},{"unstructured":"Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., Choi, Y.: COMET: commonsense transformers for automatic knowledge graph construction. In: Korhonen, A., Traum, D.R., M\u00e0rquez, L. (eds.) ACL 2019, Florence, Italy, 28 July\u20132 August 2019, Volume 1: Long Papers, pp. 4762\u20134779 (2019)","key":"52_CR2"},{"doi-asserted-by":"crossref","unstructured":"Bouraoui, Z., Camacho-Collados, J., Schockaert, S.: Inducing relational knowledge from BERT. In: AAAI 2020, IAAI 2020, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 7456\u20137463 (2020)","key":"52_CR3","DOI":"10.1609\/aaai.v34i05.6242"},{"unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6\u201312 December 2020, Virtual (2020)","key":"52_CR4"},{"doi-asserted-by":"crossref","unstructured":"Davison, J., Feldman, J., Rush, A.M.: Commonsense knowledge mining from pretrained models. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP 2019, Hong Kong, China, 3\u20137 November 2019, pp. 1173\u20131178 (2019)","key":"52_CR5","DOI":"10.18653\/v1\/D19-1109"},{"unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","key":"52_CR6"},{"issue":"3","key":"52_CR7","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1162\/dint_a_00154","volume":"4","author":"H Du","year":"2022","unstructured":"Du, H., Le, Z., Wang, H., Chen, Y., Yu, J.: COKG-QA: multi-hop question answering over COVID-19 knowledge graphs. Data Intell. 4(3), 471\u2013492 (2022)","journal-title":"Data Intell."},{"issue":"4","key":"52_CR8","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1162\/dint_a_00102","volume":"3","author":"Y Gong","year":"2021","unstructured":"Gong, Y., Mao, L., Li, C.: Few-shot learning for named entity recognition based on BERT and two-level model fusion. Data Intell. 3(4), 568\u2013577 (2021)","journal-title":"Data Intell."},{"doi-asserted-by":"crossref","unstructured":"Hwang, J.D., et al.: (comet-) atomic 2020: on symbolic and neural commonsense knowledge graphs. In: AAAI 2021, IAAI 2021, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 6384\u20136392 (2021)","key":"52_CR9","DOI":"10.1609\/aaai.v35i7.16792"},{"key":"52_CR10","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1162\/tacl_a_00324","volume":"8","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know. Trans. Assoc. Comput. Linguist. 8, 423\u2013438 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) ACL 2020, Online, 5\u201310 July 2020, pp. 7871\u20137880 (2020)","key":"52_CR11"},{"doi-asserted-by":"crossref","unstructured":"Li, X., Taheri, A., Tu, L., Gimpel, K.: Commonsense knowledge base completion. In: ACL 2016, 7\u201312 August 2016, Berlin, Germany, Volume 1: Long Papers, pp. 1445\u20131455 (2016)","key":"52_CR12","DOI":"10.18653\/v1\/P16-1137"},{"doi-asserted-by":"crossref","unstructured":"Petroni, F., et al.: Language models as knowledge bases? In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP 2019, Hong Kong, China, 3\u20137 November 2019, pp. 2463\u20132473 (2019)","key":"52_CR13","DOI":"10.18653\/v1\/D19-1250"},{"issue":"8","key":"52_CR14","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1\u2013140:67 (2020)","key":"52_CR15"},{"unstructured":"Salazar, J., Liang, D., Nguyen, T.Q., Kirchhoff, K.: Masked language model scoring. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) ACL 2020, Online, 5\u201310 July 2020, pp. 2699\u20132712 (2020)","key":"52_CR16"},{"doi-asserted-by":"crossref","unstructured":"Sap, M., et al.: ATOMIC: an atlas of machine commonsense for if-then reasoning. In: AAAI 2019, IAAI 2019, EAAI 2019, Honolulu, Hawaii, USA, 27 January\u20131 February 2019, pp. 3027\u20133035 (2019)","key":"52_CR17","DOI":"10.1609\/aaai.v33i01.33013027"},{"doi-asserted-by":"crossref","unstructured":"Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Singh, S., Markovitch, S. (eds.) AAAI 2017, 4\u20139 February 2017, San Francisco, California, USA, pp. 4444\u20134451 (2017)","key":"52_CR18","DOI":"10.1609\/aaai.v31i1.11164"},{"issue":"3","key":"52_CR19","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11633-022-1331-6","volume":"19","author":"TX Sun","year":"2022","unstructured":"Sun, T.X., Liu, X.Y., Qiu, X.P., Huang, X.J.: Paradigm shift in natural language processing. Mach. Intell. Res. 19(3), 169\u2013183 (2022)","journal-title":"Mach. Intell. Res."},{"doi-asserted-by":"crossref","unstructured":"Wang, C., Li, J., Chen, Y., Liu, K., Zhao, J.: CN-automic: distilling Chinese commonsense knowledge from pretrained language models. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) EMNLP 2022, Abu Dhabi, United Arab Emirates, 7\u201311 December 2022, pp. 9253\u20139265. Association for Computational Linguistics (2022)","key":"52_CR20","DOI":"10.18653\/v1\/2022.emnlp-main.628"},{"doi-asserted-by":"crossref","unstructured":"Wang, C., Liang, S., Jin, Y., Wang, Y., Zhu, X., Zhang, Y.: SemEval-2020 task 4: commonsense validation and explanation. In: Herbelot, A., Zhu, X., Palmer, A., Schneider, N., May, J., Shutova, E. (eds.) COLING 2020, Barcelona (online), 12\u201313 December 2020, pp. 307\u2013321 (2020)","key":"52_CR21","DOI":"10.18653\/v1\/2020.semeval-1.39"},{"issue":"4","key":"52_CR22","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s11633-022-1410-8","volume":"20","author":"X Wang","year":"2023","unstructured":"Wang, X., et al.: Large-scale multi-modal pre-trained models: a comprehensive survey. Mach. Intell. Res. 20(4), 447\u2013482 (2023)","journal-title":"Mach. Intell. Res."},{"doi-asserted-by":"crossref","unstructured":"West, P., et al.: Symbolic knowledge distillation: from general language models to commonsense models. CoRR abs\/2110.07178 (2021)","key":"52_CR23","DOI":"10.18653\/v1\/2022.naacl-main.341"},{"unstructured":"Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., Choi, Y.: HellaSwag: can a machine really finish your sentence? In: Korhonen, A., Traum, D.R., M\u00e0rquez, L. (eds.) ACL 2019, Florence, Italy, 28 July\u20132 August 2019, Volume 1: Long Papers, pp. 4791\u20134800. Association for Computational Linguistics (2019)","key":"52_CR24"},{"doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, Y., Cui, L., Huang, D.: Evaluating commonsense in pre-trained language models. In: AAAI2020, IAAI 2020, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 9733\u20139740 (2020)","key":"52_CR25","DOI":"10.1609\/aaai.v34i05.6523"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44693-1_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T08:27:10Z","timestamp":1696840030000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44693-1_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031446924","9783031446931"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44693-1_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Foshan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2023\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"478","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}