{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T02:08:39Z","timestamp":1762999719133,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930486"},{"type":"electronic","value":"9783030930493"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93049-3_18","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T06:00:24Z","timestamp":1641016824000},"page":"215-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Disentangled Contrastive Learning for\u00a0Learning Robust Textual Representations"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shumin","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"18_CR1","unstructured":"Abe, F., Josh, A.: Understanding self-supervised and contrastive learning with bootstrap your own latent (BYOL). https:\/\/untitled-ai.github.io\/understanding-self-supervised-contrastive-learning.html"},{"key":"18_CR2","unstructured":"Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., Saunshi, N.: A theoretical analysis of contrastive unsupervised representation learning. arXiv preprint arXiv:1902.09229 (2019)"},{"key":"18_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of Machine Learning and Systems 2020, pp. 10719\u201310729 (2020)"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chi, Z., et al.: InfoXLM: an information-theoretic framework for cross-lingual language model pre-training. arXiv preprint arXiv:2007.07834 (2020)","DOI":"10.18653\/v1\/2021.naacl-main.280"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL, pp. 4171\u20134186. Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Fang, H., Xie, P.: CERT: contrastive self-supervised learning for language understanding. arXiv preprint arXiv:2005.12766 (2020)","DOI":"10.36227\/techrxiv.12308378.v1"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Giorgi, J.M., Nitski, O., Bader, G.D., Wang, B.: DeCLUTR: deep contrastive learning for unsupervised textual representations. arXiv preprint arXiv:2006.03659 (2020)","DOI":"10.18653\/v1\/2021.acl-long.72"},{"key":"18_CR8","unstructured":"Grill, J.B., et al.: Bootstrap your own latent: a new approach to self-supervised learning. arXiv preprint arXiv:2006.07733 (2020)"},{"key":"18_CR9","unstructured":"Gunel, B., Du, J., Conneau, A., Stoyanov, V.: Supervised contrastive learning for pre-trained language model fine-tuning. arXiv preprint arXiv:2011.01403 (2020)"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.B.: Momentum contrast for unsupervised visual representation learning. CoRR abs\/1911.05722 (2019)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Jin, D., Jin, Z., Zhou, J.T., Szolovits, P.: Is BERT really robust ? A strong baseline for natural language attack on text classification and entailment. arXiv:1907 (2019)","DOI":"10.1609\/aaai.v34i05.6311"},{"key":"18_CR12","unstructured":"Li, L., Qiu, X.: TextAT: adversarial training for natural language understanding with token-level perturbation. arXiv preprint arXiv:2004.14543 (2020)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Normal vs. adversarial: salience-based analysis of adversarial samples for relation extraction. arXiv preprint arXiv:2104.00312 (2021)","DOI":"10.1145\/3502223.3502237"},{"key":"18_CR14","unstructured":"Liu, T., Moore, A.W., Yang, K., Gray, A.G.: An investigation of practical approximate nearest neighbor algorithms. In: Advances in Neural Information Processing Systems, pp. 825\u2013832 (2005)"},{"key":"18_CR15","unstructured":"Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Meng, Y., Zhang, Y., Huang, J., Zhang, Y., Zhang, C., Han, J.: Hierarchical topic mining via joint spherical tree and text embedding. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1908\u20131917 (2020)","DOI":"10.1145\/3394486.3403242"},{"key":"18_CR17","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26, pp. 3111\u20133119 (2013)"},{"key":"18_CR18","unstructured":"Mnih, A., Kavukcuoglu, K.: Learning word embeddings efficiently with noise-contrastive estimation. In: Advances in Neural Information Processing Systems 26, pp. 2265\u20132273 (2013)"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Ren, S., Deng, Y., He, K., Che, W.: Generating natural language adversarial examples through probability weighted word saliency. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1085\u20131097 (2019)","DOI":"10.18653\/v1\/P19-1103"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Rethmeier, N., Augenstein, I.: A primer on contrastive pretraining in language processing: methods, lessons learned and perspectives. arXiv preprint arXiv:2102.12982 (2021)","DOI":"10.1145\/3561970"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Wu, T., Guestrin, C., Singh, S.: Beyond accuracy: behavioral testing of NLP models with checklist. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5\u201310 July 2020, pp. 4902\u20134912. Association for Computational Linguistics (2020). https:\/\/www.aclweb.org\/anthology\/2020.acl-main.442\/","DOI":"10.18653\/v1\/2020.acl-main.442"},{"key":"18_CR22","unstructured":"Santurkar, S., Tsipras, D., Ilyas, A., Madry, A.: How does batch normalization help optimization? In: Advances in Neural Information Processing Systems, pp. 2483\u20132493 (2018)"},{"key":"18_CR23","unstructured":"Saunshi, N., Plevrakis, O., Arora, S., Khodak, M., Khandeparkar, H.: A theoretical analysis of contrastive unsupervised representation learning. In: Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 5628\u20135637. PMLR, 9\u201315 June 2019, Long Beach, California, USA. http:\/\/proceedings.mlr.press\/v97\/saunshi19a.html"},{"key":"18_CR24","unstructured":"Shen, S., Yao, Z., Gholami, A., Mahoney, M.W., Keutzer, K.: PowerNorm: rethinking batch normalization in transformers. In: The proceedings of the International Conference on Machine Learning (ICML) (2020)"},{"key":"18_CR25","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. CoRR abs\/1906.05849 (2019). arxiv:1906.05849"},{"key":"18_CR26","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: 7th International Conference on Learning Representations, ICLR 2019. OpenReview.net (2019). https:\/\/openreview.net\/forum?id=rJ4km2R5t7"},{"key":"18_CR27","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. arXiv preprint arXiv:2005.10242 (2020)"},{"key":"18_CR28","unstructured":"Wei, X., Hu, Y., Weng, R., Xing, L., Yu, H., Luo, W.: On learning universal representations across languages. arXiv preprint arXiv:2007.15960 (2020)"},{"key":"18_CR29","unstructured":"Wen, Y., Li, S., Jia, K.: Towards understanding the regularization of adversarial robustness on neural networks (2019)"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 3733\u20133742. IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"18_CR31","unstructured":"Wu, Z., Wang, S., Gu, J., Khabsa, M., Sun, F., Ma, H.: CLEAR: contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466 (2020)"},{"key":"18_CR32","unstructured":"Xiong, L., et al.: Approximate nearest neighbor negative contrastive learning for dense text retrieval. arXiv preprint arXiv:2007.00808 (2020)"},{"key":"18_CR33","unstructured":"Ye, H., et al.: Contrastive triple extraction with generative transformer. arXiv preprint arXiv:2009.06207 (2020)"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, X., Yuen, P.C., Chang, S.: Unsupervised embedding learning via invariant and spreading instance feature. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, pp. 6210\u20136219. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00637"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93049-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T11:36:20Z","timestamp":1674300980000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93049-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930486","9783030930493"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93049-3_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"105","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":"34% - 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":"3.2","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":"5.3","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)"}}]}}