{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:44:14Z","timestamp":1760132654025},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tkde.2021.3118469","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T18:37:00Z","timestamp":1633891020000},"page":"1-1","source":"Crossref","is-referenced-by-count":18,"title":["Sequence Labeling with Meta-Learning"],"prefix":"10.1109","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[]},{"given":"Peng","family":"Han","sequence":"additional","affiliation":[]},{"given":"Xiangnan","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Jilin","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Lisi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Shang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380127"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981314"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1075\/li.30.1.03nad"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1061"},{"key":"ref5","first-page":"5467","article-title":"A novel bi-directional interrelated model for joint intent detection and slot filling","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics","author":"Haihong"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1571941.1571989"},{"key":"ref7","article-title":"Summarization system integrated with named entity tagging and IE pattern discovery","volume-title":"Proc. 3rd Int. Conf. Lang. Resour. Eval.","author":"Nobata"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p16-1101"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref10","article-title":"Bidirectional LSTM-CRF models for sequence tagging","author":"Huang","year":"2015"},{"key":"ref11","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12006"},{"key":"ref14","first-page":"1896","article-title":"Robust lexical features for improved neural network named-entity recognition","volume-title":"Proc. Int. Conf. Comput. Linguistics","author":"Ghaddar"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2457465.2457467"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1087"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1046"},{"key":"ref18","article-title":"Transfer learning for named-entity recognition with neural networks","volume-title":"Proc. 11th Int. Conf. Lang. Res. Eval.","author":"Lee"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1226"},{"key":"ref20","article-title":"Transfer learning for sequences via learning to collocate","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cui"},{"key":"ref21","article-title":"Meta-learning with latent embedding optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rusu"},{"key":"ref22","first-page":"1","article-title":"On learning how to learn learning strategies","author":"Schmidhuber","year":"1995"},{"key":"ref23","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref24","first-page":"1920","article-title":"Online meta-learning","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref25","first-page":"7343","article-title":"Bayesian model-agnostic meta-learning","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Yoon"},{"key":"ref26","first-page":"7045","article-title":"Hierarchically structured meta-learning","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Yao"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1253"},{"key":"ref28","first-page":"3915","article-title":"Feature-critic networks for heterogeneous domain generalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref29","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2005.03.001"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1283"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1134"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1282"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/702"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/579"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.248"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty869"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1014"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6466"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1074"},{"key":"ref42","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"ref44","first-page":"1974","article-title":"Transfer learning for entity recognition of novel classes","volume-title":"Proc. 27th Int. Conf. Comput. Linguistics","author":"Rodr\u00edguez"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/566"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1001"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty449"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1236"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1336"},{"key":"ref50","article-title":"Transfer learning for sequence tagging with hierarchical recurrent networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yang"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4422"},{"key":"ref52","volume-title":"Learning to Learn","author":"Pratt","year":"1998"},{"key":"ref53","first-page":"4080","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref54","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi"},{"key":"ref55","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. 33rd Int. Conf. Int. Conf. Mach. Learn.","author":"Santoro"},{"key":"ref56","article-title":"Meta-learning with implicit gradients","author":"Rajeswaran","year":"2019"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00760"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1398"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2115"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1589"},{"key":"ref61","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3297280.3297378"},{"key":"ref63","article-title":"Generalizing across domains via cross-gradient training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shankar"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/4358933\/09563226.pdf?arnumber=9563226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T22:42:44Z","timestamp":1705012964000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9563226\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tkde.2021.3118469","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}