{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:53:43Z","timestamp":1776783223487,"version":"3.51.2"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819724208","type":"print"},{"value":"9789819724215","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-981-97-2421-5_21","type":"book-chapter","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:01:48Z","timestamp":1715414508000},"page":"312-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Continual Few-Shot Relation Extraction with\u00a0Prompt-Based Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Fei","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,12]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neucom.2020.07.077","volume":"418","author":"N Pang","year":"2020","unstructured":"Pang, N., Tan, Z., Zhao, X., Zeng, W., Xiao, W.: Domain relation extraction from noisy Chinese texts. Neurocomputing 418, 21\u201335 (2020)","journal-title":"Neurocomputing"},{"key":"21_CR2","first-page":"2536","volume":"56","author":"P Huang","year":"2019","unstructured":"Huang, P., Fang, Y., Zhu, H., Xiao, W.: End-to-end knowledge triplet extraction combined with adversarial training. J. Comput. Res. Develop. 56, 2536\u20132548 (2019)","journal-title":"J. Comput. Res. Develop."},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1715\u20131725. Berlin, Germany (2016)","DOI":"10.18653\/v1\/P16-1162"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Han, X.: Continual relation learning via episodic memory activation and reconsolidation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6429\u20136440 (2020)","DOI":"10.18653\/v1\/2020.acl-main.573"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Wu, T., et al.: Curriculum-meta learning for order-robust continual relation extraction. In: AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i12.17241"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Qin, C., Joty, S.: Continual few-shot relation learning via embedding space regularization and data augmentation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2776\u20132789. Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.acl-long.198"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of Learning and Motivation, vol. 24, pp. 109\u2013165. Elsevier (1989)","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"21_CR8","unstructured":"Parnami, A., Lee, M.: Learning from few examples: a summary of approaches to few-shot learning. arXiv:2203.04291 (2022)"},{"key":"21_CR9","unstructured":"Ritter, H., Botev, A., Barber, D.: Online structured laplace approximations for overcoming catastrophic forgetting. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Mallya, A., Davis, D., Lazebnik, S.: Piggyback: adapting a single network to multiple tasks by learning to mask weights. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 67\u201382 (2018)","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"21_CR11","unstructured":"Sun, F., Ho, C., Lee, H.: LAMOL: language modeling for lifelong language learning. In: 8th International Conference on Learning Representations, ICLR 2020. Addis Ababa, Ethiopia (2020)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Cui, L., et al.: Refining sample embeddings with relation prototypes to enhance continual relation extraction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 232\u2013243 (2021)","DOI":"10.18653\/v1\/2021.acl-long.20"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Zhao, K., Xu, H., Yang, J., et al.: Consistent representation learning for continual relation extraction. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 3402\u20133411. Dublin, Ireland (2022)","DOI":"10.18653\/v1\/2022.findings-acl.268"},{"key":"21_CR14","unstructured":"Chen, K., Lee, C.G.: Incremental few-shot learning via vector quantization in deep embedded space. In: International Conference on Learning Representations (2021)"},{"key":"21_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2020.584192","volume":"14","author":"N Pang","year":"2020","unstructured":"Pang, N., Tan, Z., Hao, X., Xiao, W.: Boosting knowledge base automatically via few-shot relation classification. Front. Neurorobot. 14, 584192 (2020)","journal-title":"Front. Neurorobot."},{"key":"21_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-3055-1","volume":"64","author":"N Pang","year":"2021","unstructured":"Pang, N., Zhao, X., Wang, W., Xiao, W., Guo, D.: Few-shot text classification by leveraging bi-directional attention and cross-class knowledge. Sci. China Inf. Sci. 64, 1\u201313 (2021)","journal-title":"Sci. China Inf. Sci."},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Fei, J., Zeng, W., Zhao, X., Li, X., Xiao, W.: Few-shot relational triple extraction with perspective transfer network. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 488\u2013498 (2022)","DOI":"10.1145\/3511808.3557323"},{"key":"21_CR18","unstructured":"Zhang, L., Deng, Z., Kawaguchi, K., Ghorbani, A., Zou, J.: How does mixup help with robustness and generalization? arXiv:2010.04819 (2020)"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Girshick, R., Doll\u00e1r, P.: Rethinking imagenet pre-training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4918\u20134927 (2019)","DOI":"10.1109\/ICCV.2019.00502"},{"key":"21_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108202","volume":"166","author":"W Jingyao","year":"2020","unstructured":"Jingyao, W., Zhao, Z., Sun, C., Yan, R., Chen, X.: Few-shot transfer learning for intelligent fault diagnosis of machine. Measurement 166, 108202 (2020)","journal-title":"Measurement"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Tian, R., Shi, H.: Momentum memory contrastive learning for transfer-based few-shot classification. In: Applied Intelligence, pp. 1\u201315 (2022)","DOI":"10.1007\/s10489-022-03506-3"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Gao, B., Zhao, X., Zhao, H.: An active and contrastive learning framework for fine-grained off-road semantic segmentation. In: IEEE Transactions on Intelligent Transportation Systems (2022)","DOI":"10.1109\/IROS51168.2021.9636033"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction. In: Proceedings of the ACM Web Conference 2022, pp. 2778\u20132788 (2022)","DOI":"10.1145\/3485447.3511998"},{"issue":"9","key":"21_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P., Yuan, W., Jinlan, F., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9), 1\u201335 (2023)","journal-title":"ACM Comput. Surv."},{"key":"21_CR25","unstructured":"Scao, T.L., Rush, A.M.: How many data points is a prompt worth? arXiv:2103.08493 (2021)"},{"key":"21_CR26","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"21_CR27","unstructured":"Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: Distributional similarity for relation learning. arXiv:1906.03158 (2019)"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Han, X., et al.: Fewrel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. arXiv:1810.10147 (2018)","DOI":"10.18653\/v1\/D18-1514"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Conference on Empirical Methods in Natural Language Processing (2017)","DOI":"10.18653\/v1\/D17-1004"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2421-5_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T08:06:31Z","timestamp":1715414791000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2421-5_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819724208","9789819724215"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2421-5_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"12 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","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":"6 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","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":"apwebwaim2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apweb-waim2023.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}