{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:19:52Z","timestamp":1742984392393,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981809"},{"type":"electronic","value":"9789819981816"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8181-6_29","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:30Z","timestamp":1701039750000},"page":"377-390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Informative Prompt Learning for\u00a0Low-Shot Commonsense Question Answering via\u00a0Fine-Grained Redundancy Reduction"],"prefix":"10.1007","author":[{"given":"Zhikai","family":"Lei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"29_CR1","first-page":"217","volume":"1","author":"H Barlow","year":"1961","unstructured":"Barlow, H.: Possible principles underlying the transformations of sensory messages. Sens. Commun. 1, 217\u2013233 (1961)","journal-title":"Sens. Commun."},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jmva.2015.02.003","volume":"137","author":"TT Cai","year":"2015","unstructured":"Cai, T.T., Liang, T., Zhou, H.H.: Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional gaussian distributions. J. Multivar. Anal. 137, 161\u2013172 (2015)","journal-title":"J. Multivar. Anal."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Dalvi, F., Sajjad, H., Durrani, N., Belinkov, Y.: Analyzing redundancy in pretrained transformer models. In: EMNLP, pp. 4908\u20134926. ACL (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.398"},{"key":"29_CR4","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, pp. 4171\u20134186. ACL (2019)"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Dou, Z., Peng, N.: Zero-shot commonsense question answering with cloze translation and consistency optimization. ArXiv (2022)","DOI":"10.1609\/aaai.v36i10.21301"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wu, A., Zhou, J., Gu, Y., Zhao, Y., Cheng, G.: Clues before answers: generation-enhanced multiple-choice QA. ArXiv (2022)","DOI":"10.18653\/v1\/2022.naacl-main.239"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Kovaleva, O., Romanov, A., Rogers, A., Rumshisky, A.: Revealing the dark secrets of BERT. In: EMNLP\/IJCNLP, pp. 4364\u20134373. ACL (2019)","DOI":"10.18653\/v1\/D19-1445"},{"key":"29_CR8","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. In: ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Lin, B.Y., Chen, X., Chen, J., Ren, X.: KagNet: knowledge-aware graph networks for commonsense reasoning. In: EMNLP\/IJCNLP, pp. 2829\u20132839. ACL (2019)","DOI":"10.18653\/v1\/D19-1282"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Ma, K., Ilievski, F., Francis, J., Bisk, Y., Nyberg, E., Oltramari, A.: Knowledge-driven data construction for zero-shot evaluation in commonsense question answering. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i15.17593"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Mihaylov, T., Clark, P., Khot, T., Sabharwal, A.: Can a suit of armor conduct electricity? A new dataset for open book question answering. In: EMNLP, pp. 2381\u20132391. ACL (2018)","DOI":"10.18653\/v1\/D18-1260"},{"key":"29_CR12","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. ArXiv (2019)"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Sap, M., et al.: ATOMIC: an atlas of machine commonsense for if-then reasoning. In: AAAI, pp. 3027\u20133035. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33013027"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Sap, M., Rashkin, H., Chen, D., Bras, R.L., Choi, Y.: Social IQA: commonsense reasoning about social interactions. In: EMNLP, pp. 4462\u20134472. ACL (2019)","DOI":"10.18653\/v1\/D19-1454"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Exploiting cloze-questions for few-shot text classification and natural language inference. In: EACL, pp. 255\u2013269. ACL (2021)","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Shwartz, V., West, P., Bras, R.L., Bhagavatula, C., Choi, Y.: Unsupervised commonsense question answering with self-talk. In: EMNLP, pp. 4615\u20134629. ACL(2020)","DOI":"10.18653\/v1\/2020.emnlp-main.373"},{"key":"29_CR17","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, pp. 4444\u20134451. AAAI Press (2017)","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"29_CR18","unstructured":"Talmor, A., Herzig, J., Lourie, N., Berant, J.: CommonsenseQA: a question answering challenge targeting commonsense knowledge. In: NAACL-HLT, pp. 4149\u20134158. ACL (2019)"},{"key":"29_CR19","unstructured":"TISHBY, N.: The information bottleneck method. In: Proceedings of the 37th Annual Allerton Conference on Communications, Control and Computing, 1999, pp. 368\u2013377 (1999)"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Tishby, N., Zaslavsky, N.: Deep learning and the information bottleneck principle. In: 2015 IEEE Information Theory Workshop (ITW)","DOI":"10.1109\/ITW.2015.7133169"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Wang, P., Peng, N., Ilievski, F., Szekely, P.A., Ren, X.: Connecting the dots: a knowledgeable path generator for commonsense question answering. In: EMNLP (Findings). Findings of ACL, vol. EMNLP 2020, pp. 4129\u20134140. ACL (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.369"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J.: QA-GNN: reasoning with language models and knowledge graphs for question answering. In: NAACL-HLT, pp. 535\u2013546. ACL (2021)","DOI":"10.18653\/v1\/2021.naacl-main.45"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Yoo, D., Fan, H., Boddeti, V.N., Kitani, K.M.: Efficient k-shot learning with regularized deep networks. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second (AAAI-18), pp. 4382\u20134389. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.11774"},{"key":"29_CR24","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: ICML (2021)"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8181-6_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:30:18Z","timestamp":1710329418000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8181-6_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981809","9789819981816"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8181-6_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}