{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:41:00Z","timestamp":1760013660161,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954666"},{"type":"electronic","value":"9783030954673"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-95467-3_18","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"235-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-6399","authenticated-orcid":false,"given":"Rutuja","family":"Taware","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2925-4413","authenticated-orcid":false,"given":"Shraddha","family":"Varat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4547-3955","authenticated-orcid":false,"given":"Gaurav","family":"Salunke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7886-1121","authenticated-orcid":false,"given":"Chaitanya","family":"Gawande","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9258-4984","authenticated-orcid":false,"given":"Geetanjali","family":"Kale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0367-8813","authenticated-orcid":false,"given":"Rahul","family":"Khengare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1892-1812","authenticated-orcid":false,"given":"Raviraj","family":"Joshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Conneau, A., Kruszewski, G., Lample, G., Barrault, L., Baroni, M.: What you can cram into a single vector: probing sentence embeddings for linguistic properties. arXiv preprint arXiv:1805.01070 (2018)","DOI":"10.18653\/v1\/P18-1198"},{"key":"18_CR2","unstructured":"Desai, U., Tamilselvam, S., Kaur, J., Mani, S., Khare, S.: Benchmarking popular classification models\u2019 robustness to random and targeted corruptions (2020)"},{"key":"18_CR3","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018). http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"DeYoung, J., et al.: Eraser: a benchmark to evaluate rationalized NLP models. arXiv preprint arXiv:1911.03429 (2019)","DOI":"10.18653\/v1\/2020.acl-main.408"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Ebrahimi, J., Rao, A., Lowd, D., Dou, D.: HotFlip: white-box adversarial examples for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 31\u201336. Association for Computational Linguistics, Melbourne, Australia, July 2018. https:\/\/doi.org\/10.18653\/v1\/P18-2006, https:\/\/www.aclweb.org\/anthology\/P18-2006","DOI":"10.18653\/v1\/P18-2006"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Feng, S., Wallace, E., Grissom II, A., Iyyer, M., Rodriguez, P., Boyd-Graber, J.: Pathologies of neural models make interpretations difficult. arXiv preprint arXiv:1804.07781 (2018)","DOI":"10.18653\/v1\/D18-1407"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Gao, J., Lanchantin, J., Soffa, M.L., Qi, Y.: Black-box generation of adversarial text sequences to evade deep learning classifiers. CoRR abs\/1801.04354 (2018). http:\/\/arxiv.org\/abs\/1801.04354","DOI":"10.1109\/SPW.2018.00016"},{"key":"18_CR8","unstructured":"Howard, J., Ruder, S.: Fine-tuned language models for text classification. CoRR abs\/1801.06146 (2018). http:\/\/arxiv.org\/abs\/1801.06146"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Jacovi, A., Shalom, O.S., Goldberg, Y.: Understanding convolutional neural networks for text classification. arXiv preprint arXiv:1809.08037 (2018)","DOI":"10.18653\/v1\/W18-5408"},{"key":"18_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/978-3-030-44689-5_9","volume-title":"Intelligent Human Computer Interaction","author":"R Joshi","year":"2020","unstructured":"Joshi, R., Goel, P., Joshi, R.: Deep learning for Hindi text classification: a comparison. In: Tiwary, U.S., Chaudhury, S. (eds.) IHCI 2019. LNCS, vol. 11886, pp. 94\u2013101. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44689-5_9"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"issue":"4","key":"18_CR12","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/info10040150","volume":"10","author":"K Kowsari","year":"2019","unstructured":"Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 10(4), 150 (2019)","journal-title":"Information"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Mandhane, M., Likhitkar, M., Kshirsagar, G., Jagdale, J., Joshi, R.: Experimental evaluation of deep learning models for Marathi text classification. arXiv preprint arXiv:2101.04899 (2021)","DOI":"10.1007\/978-981-16-6407-6_53"},{"key":"18_CR14","unstructured":"Li, J., Monroe, W., Jurafsky, D.: Understanding neural networks through representation erasure. arXiv preprint arXiv:1612.08220 (2016)"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Li, X., Roth, D.: Learning question classifiers. In: COLING 2002: The 19th International Conference on Computational Linguistics (2002). https:\/\/www.aclweb.org\/anthology\/C02-1150","DOI":"10.3115\/1072228.1072378"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Liang, B., Li, H., Su, M., Bian, P., Li, X., Shi, W.: Deep text classification can be fooled. CoRR abs\/1704.08006 (2017). http:\/\/arxiv.org\/abs\/1704.08006","DOI":"10.24963\/ijcai.2018\/585"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Liu, B., Lane, I.: Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv preprint arXiv:1609.01454 (2016)","DOI":"10.21437\/Interspeech.2016-1352"},{"key":"18_CR18","unstructured":"NA: Amazon confuses sarcastic tweet on maharashtra turmoil for customer complaint, deletes it later. https:\/\/www.news18.com\/news\/buzz\/amazon-help-confuses-sarcastic-tweet-on-maharashtra-political-crisis-for-customer-complaint-deletes-tweet-later-2399797.html. Accessed 31 Dec 2020"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427\u2013436 (2015)","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Semantically equivalent adversarial rules for debugging NLP models. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 856\u2013865 (2018)","DOI":"10.18653\/v1\/P18-1079"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Wu, T., Guestrin, C., Singh, S.: Beyond accuracy: behavioral testing of NLP models with checklist. arXiv preprint arXiv:2005.04118 (2020)","DOI":"10.24963\/ijcai.2021\/659"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Rios, A., Kavuluru, R.: Convolutional neural networks for biomedical text classification: application in indexing biomedical articles. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 258\u2013267 (2015)","DOI":"10.1145\/2808719.2808746"},{"key":"18_CR24","unstructured":"Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631\u20131642. Association for Computational Linguistics, Seattle, Washington, USA, October 2013. https:\/\/www.aclweb.org\/anthology\/D13-1170"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Wallace, E., Tuyls, J., Wang, J., Subramanian, S., Gardner, M., Singh, S.: Allennlp interpret: a framework for explaining predictions of NLP models. arXiv preprint arXiv:1909.09251 (2019)","DOI":"10.18653\/v1\/D19-3002"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Yuan, H., Chen, Y., Hu, X., Ji, S.: Interpreting deep models for text analysis via optimization and regularization methods. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5717\u20135724 (2019)","DOI":"10.1609\/aaai.v33i01.33015717"},{"key":"18_CR27","unstructured":"Zhou, C., Sun, C., Liu, Z., Lau, F.: A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630 (2015)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95467-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:30:38Z","timestamp":1680769838000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95467-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954666","9783030954673"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95467-3_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"215","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":"86","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":"40% - 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":"5-6","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":"1-2","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)"}}]}}