{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T14:32:34Z","timestamp":1744209154359,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"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":["Appl Intell"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10489-020-01991-y","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T07:02:48Z","timestamp":1604386968000},"page":"2377-2392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust dialog state tracker with contextual-feature augmentation"],"prefix":"10.1007","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9682-536X","authenticated-orcid":false,"given":"Xuejun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"1991_CR1","unstructured":"Antoniou A, Edwards H, Storkey A (2018) How to train your maml. In: International Conference on Learning Representations (ICLR), pp 770\u2013774"},{"key":"1991_CR2","unstructured":"Arpit D, Jastrzbski S, Ballas N, Krueger D, Bengio E, Kanwal MS, Maharaj T, Fischer A, Courville A, Bengio Y, et al. (2017) A closer look at memorization in deep networks. In: Proceedings of the 34th International Conference on Machine Learning (ICML), pp 233\u2013242"},{"key":"1991_CR3","doi-asserted-by":"crossref","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai AM, J\u00f3zefowicz R, Bengio S (2016) Generating sentences from a continuous space. In: CoNLL, pp 10\u201321","DOI":"10.18653\/v1\/K16-1002"},{"key":"1991_CR4","doi-asserted-by":"crossref","unstructured":"Budzianowski P, Wen TH, Tseng BH, Casanueva I, Ultes S, Ramadan O, Gasic M (2018) Multiwoz - a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling. In: Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP), pp 5016\u20135026","DOI":"10.18653\/v1\/D18-1547"},{"key":"1991_CR5","unstructured":"Chao GL, Lane I (2019) Bert-dst: Scalable end-to-end dialogue state tracking with bidirectional encoder representations from transformer. In: INTERSPEECH, pp 1468\u20131472"},{"key":"1991_CR6","doi-asserted-by":"crossref","unstructured":"Chen L, Lv B, Wang C, Zhu S, Tan B, Yu K (2020) Schema-guided multi-domain dialogue state tracking with graph attention neural networks. In: AAAI, pp 7521\u20137528","DOI":"10.1609\/aaai.v34i05.6250"},{"key":"1991_CR7","unstructured":"Eric M, Goel R, Paul S, Kumar A, Sethi A, Goyal AK, Ku P, Agarwal S, Gao S (2020) Multiwoz 2.1: A consolidated multi-domain dialogue dataset with state corrections and state tracking baselines. In: LREC"},{"issue":"2-3","key":"1991_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/1500000074","volume":"13","author":"J Gao","year":"2019","unstructured":"Gao J, Galley M, Li L, et al. (2019) Neural approaches to conversational ai. Foundations and Trends\u00ae;, in Information Retrieval 13(2-3):127\u2013298","journal-title":"Foundations and Trends\u00ae;, in Information Retrieval"},{"key":"1991_CR9","doi-asserted-by":"publisher","first-page":"105448","DOI":"10.1016\/j.knosys.2019.105448","volume":"193","author":"P Gao","year":"2020","unstructured":"Gao P, Yuan R, Wang F, Xiao L, Fujita H, Zhang Y (2020) Siamese attentional keypoint network for high performance visual tracking. Knowl.-Based Syst 193:105448","journal-title":"Knowl.-Based Syst"},{"key":"1991_CR10","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ins.2019.12.084","volume":"517","author":"P Gao","year":"2020","unstructured":"Gao P, Zhang Q, Wang F, Xiao L, Fujita H, Zhang Y (2020) Learning reinforced attentional representation for end-to-end visual tracking. Inform Sci 517:52\u201367","journal-title":"Inform Sci"},{"key":"1991_CR11","doi-asserted-by":"crossref","unstructured":"Gao S, Sethi A, Agarwal S, Chung T, Hakkani-Tur D (2019) Dialog state tracking: A neural reading comprehension approach. In: Proceedings of the 20th annual meeting of the special interest group on discourse and dialogue (SIGDIAL), pp 264\u2013273","DOI":"10.18653\/v1\/W19-5932"},{"key":"1991_CR12","doi-asserted-by":"crossref","unstructured":"Goel R, Paul S, Hakkani-T\u00fcr DZ (2019) Hyst: A hybrid approach for flexible and accurate dialogue state tracking. In: INTERSPEECH, pp 1458\u20131462","DOI":"10.21437\/Interspeech.2019-1863"},{"key":"1991_CR13","unstructured":"Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: Robust training of deep neural networks with extremely noisy labels. In: Advances in neural information processing systems, pp 8527\u20138537"},{"key":"1991_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1991_CR15","doi-asserted-by":"crossref","unstructured":"Henderson M, Ga\u0161i\u0107 M, Thomson B, Tsiakoulis P, Yu K, Young S (2012) Discriminative spoken language understanding using word confusion networks. In: 2012 IEEE Spoken Language Technology Workshop (SLT). IEEE, pp 176\u2013181","DOI":"10.1109\/SLT.2012.6424218"},{"key":"1991_CR16","doi-asserted-by":"crossref","unstructured":"Henderson M, Thomson B, Young S (2014) Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation. In: 2014 IEEE Spoken Language Technology Workshop (SLT). IEEE, pp 360\u2013365","DOI":"10.1109\/SLT.2014.7078601"},{"key":"1991_CR17","doi-asserted-by":"crossref","unstructured":"Henderson M, Thomson B, Young S (2014) Word-based dialog state tracking with recurrent neural networks. In: Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pp 292\u2013299","DOI":"10.3115\/v1\/W14-4340"},{"key":"1991_CR18","unstructured":"Le H, Socher R, Hoi SC (2019) Non-autoregressive dialog state tracking. In: International Conference on Learning Representations (ICLR), pp 146\u2013150"},{"key":"1991_CR19","doi-asserted-by":"crossref","unstructured":"Lei W, Jin X, Kan MY, Ren Z, He X, Yin D (2018) Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures. In: ACL, pp 1437\u20131447","DOI":"10.18653\/v1\/P18-1133"},{"key":"1991_CR20","doi-asserted-by":"crossref","unstructured":"Li Y, Yang J, Song Y, Cao L, Luo J, Li LJ (2017) Learning from noisy labels with distillation. In: Proceedings of the IEEE international conference on computer vision, pp 1910\u20131918","DOI":"10.1109\/ICCV.2017.211"},{"key":"1991_CR21","unstructured":"Ma X, Wang Y, Houle ME, Zhou S, Erfani S, Xia S, Wijewickrema S, Bailey J (2018) Dimensionality-driven learning with noisy labels. In: International Conference on Machine Learning (ICML), pp 3355\u20133364"},{"key":"1991_CR22","doi-asserted-by":"crossref","unstructured":"Mrk\u0161i\u0107 N, \u00d3 S\u00e9aghdha D, Wen TH, Thomson B, Young S (2017) Neural belief tracker: Data-driven dialogue state tracking. In: ACL, pp 1777\u20131788","DOI":"10.18653\/v1\/P17-1163"},{"key":"1991_CR23","doi-asserted-by":"crossref","unstructured":"Mrk\u0161i\u0107 N, S\u00e9aghdha DO, Thomson B, Ga\u0161i\u0107 M, Su PH, Vandyke D, Wen TH, Young S (2015) Multi-domain dialog state tracking using recurrent neural networks. In: ACL, pp 794\u2013799","DOI":"10.3115\/v1\/P15-2130"},{"key":"1991_CR24","unstructured":"Nouri E, Hosseini-Asl E (2018) Toward scalable neural dialogue state tracking model. In: Advances in neural information processing systems (NeurIPS), 2nd Conversational AI workshop"},{"key":"1991_CR25","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"1991_CR26","doi-asserted-by":"crossref","unstructured":"Perez J, Liu F (2017) Dialog state tracking, a machine reading approach using memory network. In: the European Chapter of the Association for Computational Linguistics (EACL), pp 305\u2013314","DOI":"10.18653\/v1\/E17-1029"},{"key":"1991_CR27","doi-asserted-by":"crossref","unstructured":"Ramadan O, Budzianowski P, Ga\u0161i\u0107 M. (2018) Large-scale multi-domain belief tracking with knowledge sharing. In: ACL, pp 432\u2013437","DOI":"10.18653\/v1\/P18-2069"},{"key":"1991_CR28","doi-asserted-by":"crossref","unstructured":"Ren L, Xie K, Chen L, Yu K (2018) Towards universal dialogue state tracking. In: ACL, pp 2780\u20132786","DOI":"10.18653\/v1\/D18-1299"},{"key":"1991_CR29","unstructured":"Ren M, Zeng W, Yang B, Urtasun R (2018) Learning to reweight examples for robust deep learning. In: International Conference on Machine Learning (ICML), pp 4334\u20134343"},{"key":"1991_CR30","doi-asserted-by":"crossref","unstructured":"Rodrigues F, Pereira FC (2018) Deep learning from crowds. In: AAAI, pp 552\u2013560","DOI":"10.1609\/aaai.v32i1.11506"},{"key":"1991_CR31","doi-asserted-by":"crossref","unstructured":"Tanaka D, Ikami D, Yamasaki T, Aizawa K (2018) Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5552\u20135560","DOI":"10.1109\/CVPR.2018.00582"},{"issue":"4","key":"1991_CR32","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.csl.2009.07.003","volume":"24","author":"B Thomson","year":"2010","unstructured":"Thomson B, Young S (2010) Bayesian update of dialogue state: A pomdp framework for spoken dialogue systems. Computer Speech & Language 24(4):562\u2013588","journal-title":"Computer Speech & Language"},{"key":"1991_CR33","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems (NIPS), pp 5998\u20136008"},{"key":"1991_CR34","doi-asserted-by":"crossref","unstructured":"Veit A, Alldrin N, Chechik G, Krasin I, Gupta A, Belongie S (2017) Learning from noisy large-scale datasets with minimal supervision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 839\u2013847","DOI":"10.1109\/CVPR.2017.696"},{"key":"1991_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Liu W, Ma X, Bailey J, Zha H, Song L, Xia ST (2018) Iterative learning with open-set noisy labels. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8688\u20138696","DOI":"10.1109\/CVPR.2018.00906"},{"key":"1991_CR36","unstructured":"Wang Z, Lemon O (2013) A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information. In: Proceedings of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pp 423\u2013432"},{"key":"1991_CR37","doi-asserted-by":"crossref","unstructured":"Wen TH, Vandyke D, Mrk\u0161i\u0107 N, Gasic M, Rojas Barahona LM, Su PH, Ultes S, Young S (2017) A network-based end-to-end trainable task-oriented dialogue system. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp 438\u2013449","DOI":"10.18653\/v1\/E17-1042"},{"key":"1991_CR38","doi-asserted-by":"crossref","unstructured":"Williams JD (2014) Web-style ranking and slu combination for dialog state tracking. In: Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pp 282\u2013291","DOI":"10.3115\/v1\/W14-4339"},{"key":"1991_CR39","unstructured":"Wu CS, Madotto A, Hosseini-Asl E, Xiong C, Socher R, Fung P (2019) Transferable multi-domain state generator for task-oriented dialogue systems. In: ACL, pp 808\u2013819"},{"key":"1991_CR40","doi-asserted-by":"crossref","unstructured":"Xu M, Wang Y, Chi Y, Hua X (2020) Training liver vessel segmentation deep neural networks on noisy labels from contrast ct imaging. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, pp 1552\u20131555","DOI":"10.1109\/ISBI45749.2020.9098509"},{"key":"1991_CR41","doi-asserted-by":"crossref","unstructured":"Xu P, Hu Q (2018) An end-to-end approach for handling unknown slot values in dialogue state tracking. In: ACL, pp 1448\u20131457","DOI":"10.18653\/v1\/P18-1134"},{"issue":"5","key":"1991_CR42","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/JPROC.2012.2225812","volume":"101","author":"S Young","year":"2013","unstructured":"Young S, Ga\u0161i\u0107 M, Thomson B, Williams JD (2013) Pomdp-based statistical spoken dialog systems: A review. Proceedings of the IEEE 101(5):1160\u20131179","journal-title":"Proceedings of the IEEE"},{"key":"1991_CR43","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2017) Understanding deep learning requires rethinking generalization. International Conference on Learning Representations (ICLR), pp 203\u2013207"},{"key":"1991_CR44","unstructured":"Zhang JG, Hashimoto K, Wu CS, Wan Y, Yu PS, Socher R, Xiong C (2019) Find or classify? dual strategy for slot-value predictions on multi-domain dialog state tracking. In: Advances in neural information processing systems (NeurIPS), Conversational AI workshop"},{"key":"1991_CR45","doi-asserted-by":"crossref","unstructured":"Zhong V, Xiong C, Socher R (2018) Global-locally self-attentive encoder for dialogue state tracking. In: ACL, pp 1458\u20131467","DOI":"10.18653\/v1\/P18-1135"},{"key":"1991_CR46","unstructured":"Zhou L, Small K (2019) Multi-domain dialogue state tracking as dynamic knowledge graph enhanced question answering. In: Advances in neural information processing systems (NeurIPS), Conversational AI workshop"},{"key":"1991_CR47","doi-asserted-by":"crossref","unstructured":"Zilka L, Jurcicek F (2015) Incremental lstm-based dialog state tracker. In: 2015 Ieee Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, pp 757\u2013762","DOI":"10.1109\/ASRU.2015.7404864"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01991-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-020-01991-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01991-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T09:07:52Z","timestamp":1669453672000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-020-01991-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,3]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1991"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01991-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2020,11,3]]},"assertion":[{"value":"28 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}