{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:05:07Z","timestamp":1743069907055,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030624590"},{"type":"electronic","value":"9783030624606"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62460-6_30","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"338-346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Counterfactual Retrieval for\u00a0Augmentation and Decisions"],"prefix":"10.1007","author":[{"given":"Nwaike","family":"Kelechi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"2","key":"30_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.5465\/1976.4408670","volume":"1","author":"PC Nutt","year":"1976","unstructured":"Nutt, P.C.: Models for decision making in organizations and some contextual variables which stipulate optimal use. Acad. Manag. Rev. 1(2), 84\u201398 (1976). https:\/\/doi.org\/10.5465\/1976.4408670","journal-title":"Acad. Manag. Rev."},{"issue":"1","key":"30_CR2","first-page":"3207","volume":"14","author":"L Bottou","year":"2013","unstructured":"Bottou, L., et al.: Counterfactual reasoning and learning systems: the example of computational advertising. J. Mach. Learn. Res. 14(1), 3207\u20133260 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"30_CR3","unstructured":"Pearl, J.: Causal and counterfactual inference. In: The Handbook of Rationality, pp. 1\u201341 (2018)"},{"key":"30_CR4","doi-asserted-by":"publisher","unstructured":"Son, Y., et al.: Recognizing counterfactual thinking in social media texts. In: ACL (2017). https:\/\/doi.org\/10.18653\/v1\/P17-2103","DOI":"10.18653\/v1\/P17-2103"},{"issue":"4","key":"30_CR5","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/S0747-5632(01)00059-0","volume":"18","author":"MT Whitty","year":"2002","unstructured":"Whitty, M.T.: Liar, liar! An examination of how open, supportive and honest people are in chat rooms. Comput. Hum. Behav. 18(4), 343\u2013352 (2002)","journal-title":"Comput. Hum. Behav."},{"key":"30_CR6","unstructured":"Hendricks, L. A., Hu, R., Darrell, T., Akata, Z.: Generating counterfactual explanations with natural language. arXiv preprint arXiv:1806.09809. (2018)"},{"key":"30_CR7","doi-asserted-by":"publisher","unstructured":"Ramaravind, K.M., Amit, S., Chenhao T.: Explaining machine learning classifiers through diverse counterfactual explanations. In Conference on Fairness, Accountability, and Transparency, 27\u201330 January (2020). https:\/\/doi.org\/10.1145\/3351095.3372850","DOI":"10.1145\/3351095.3372850"},{"key":"30_CR8","volume-title":"Counterfactuals","author":"D Lewis","year":"2013","unstructured":"Lewis, D.: Counterfactuals. John Wiley & Sons, Hoboken (2013)"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Brill, E.: A simple rule-based part of speech tagger. In: Proceedings of the Third Conference on Applied Natural Language Processing, pp. 152\u2013155. Association for Computational Linguistics (1992)","DOI":"10.3115\/974499.974526"},{"key":"30_CR10","volume-title":"Foundations of Statistical Natural Language Processing","author":"CD Manning","year":"1999","unstructured":"Manning, C.D., Manning, C.D., Sch\u00fctze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)"},{"key":"30_CR11","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"30_CR12","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding arXiv preprint: 1810.04805. (2018). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Peters, M.E., Ruder, S., Smith, N.A.: To Tune or not to tune? Adapting pretrained representations to diverse tasks. In: ACL (2019). https:\/\/doi.org\/10.18653\/v1\/W19-4302","DOI":"10.18653\/v1\/W19-4302"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Yang, X., Obadinma, S., Zhao, H., Zhang, Q., Matwin, S., Zhu, X.: SemEval-2020 Task 5: counterfactual recognition. In: Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020) (2020)","DOI":"10.18653\/v1\/2020.semeval-1.40"},{"key":"30_CR16","doi-asserted-by":"publisher","unstructured":"Russell, M.A.: Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O\u2019Reilly Media, Inc. (2013). https:\/\/doi.org\/10.1080\/15536548.2015.1046287","DOI":"10.1080\/15536548.2015.1046287"},{"key":"30_CR17","unstructured":"Nwaike, K., Jiao, L. : Counterfactual detection meets transfer learning. In: Modelling Causal Reasoning in Language: Detecting Counterfactuals at SemEval-2020 Task [5] (2020, accepted)"},{"key":"30_CR18","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.knosys.2014.11.007","volume":"79","author":"R Agerri","year":"2015","unstructured":"Agerri, R., Artola, X., Beloki, Z., Rigau, G., Soroa, A.: Big data for natural language processing: a streaming approach. Knowl.-Based Syst. 79, 36\u201342 (2015)","journal-title":"Knowl.-Based Syst."},{"key":"30_CR19","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint 1907.11692 (2019)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62460-6_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T13:30:17Z","timestamp":1669555817000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62460-6_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030624590","9783030624606"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62460-6_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","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":"360","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":"118","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":"40","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":"33% - 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":"2.2","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":"8","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)"}}]}}