{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T05:15:32Z","timestamp":1702444532465},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"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":["J Supercomput"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11227-023-05217-z","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T19:01:51Z","timestamp":1684263711000},"page":"18547-18568","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient slot correlation learning network for multi-domain dialogue state tracking"],"prefix":"10.1007","volume":"79","author":[{"given":"Qianyu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"5217_CR1","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1007\/s10462-022-10248-8","volume":"56","author":"J Ni","year":"2023","unstructured":"Ni J, Young T, Pandelea V et al (2023) Recent advances in deep learning based dialogue systems: a systematic survey. Artif Intell Rev 56:3055\u20133155. https:\/\/doi.org\/10.1007\/s10462-022-10248-8","journal-title":"Artif Intell Rev"},{"issue":"2","key":"5217_CR2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1145\/3166054.3166058","volume":"19","author":"H Chen","year":"2017","unstructured":"Chen H, Liu X, Yin D et al (2017) A survey on dialogue systems: recent advances and new frontiers. SIGKDD Explor Newsl 19(2):25\u201335. https:\/\/doi.org\/10.1145\/3166054.3166058","journal-title":"SIGKDD Explor Newsl"},{"key":"5217_CR3","doi-asserted-by":"publisher","unstructured":"Lee H, Lee J, Kim TY (2019) SUMBT: slot-utterance matching for universal and scalable belief tracking. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for computational linguistics, Florence, Italy, pp 5478\u20135483, https:\/\/doi.org\/10.18653\/v1\/P19-1546","DOI":"10.18653\/v1\/P19-1546"},{"key":"5217_CR4","doi-asserted-by":"publisher","unstructured":"Kim S, Yang S, Kim G et\u00a0al (2020) Efficient dialogue state tracking by selectively overwriting memory. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for computational linguistics, Online, pp 567\u2013582, https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.53","DOI":"10.18653\/v1\/2020.acl-main.53"},{"key":"5217_CR5","doi-asserted-by":"publisher","unstructured":"Mrk\u0161i\u0107 N, \u00d3\u00a0S\u00e9aghdha D, Wen TH et\u00a0al (2017) Neural belief tracker: data-driven dialogue state tracking. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers). Association for computational linguistics, Vancouver, Canada, pp 1777\u20131788, https:\/\/doi.org\/10.18653\/v1\/P17-1163","DOI":"10.18653\/v1\/P17-1163"},{"key":"5217_CR6","unstructured":"Dai Y, Yu H, Jiang Y et\u00a0al (2020) A survey on dialog management: recent advances and challenges. Preprint at arXiv: 2005.02233"},{"key":"5217_CR7","doi-asserted-by":"crossref","unstructured":"Balaraman V, Sheikhalishahi S, Magnini B (2021) Recent neural methods on dialogue state tracking for task-oriented dialogue systems: a survey. In: Proceedings of the 22nd annual meeting of the special interest group on discourse and dialogue. Association for computational linguistics, Singapore and Online, pp 239\u2013251","DOI":"10.18653\/v1\/2021.sigdial-1.25"},{"key":"5217_CR8","doi-asserted-by":"crossref","unstructured":"Jacqmin L, Rojas\u00a0Barahona LM, Favre B (2022) Do you follow me?: a survey of recent approaches in dialogue state tracking. In: Proceedings of the 23rd annual meeting of the special interest group on discourse and dialogue. Association for computational linguistics, Edinburgh, UK, pp 336\u2013350","DOI":"10.18653\/v1\/2022.sigdial-1.33"},{"key":"5217_CR9","doi-asserted-by":"publisher","unstructured":"Shan Y, Li Z, Zhang J et\u00a0al (2020) A contextual hierarchical attention network with adaptive objective for dialogue state tracking. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for computational linguistics, Online, pp 6322\u20136333, https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.563","DOI":"10.18653\/v1\/2020.acl-main.563"},{"key":"5217_CR10","doi-asserted-by":"crossref","unstructured":"Heck M, van Niekerk C, Lubis N et\u00a0al (2020) TripPy: a triple copy strategy for value independent neural dialog state tracking. In: Proceedings of the 21st annual meeting of the special interest group on discourse and dialogue. Association for computational linguistics, 1st virtual meeting, pp 35\u201344","DOI":"10.18653\/v1\/2020.sigdial-1.4"},{"key":"5217_CR11","doi-asserted-by":"publisher","unstructured":"Zhu S, Li J, Chen L et\u00a0al (2020) Efficient context and schema fusion networks for multi-domain dialogue state tracking. In: Findings of the association for computational linguistics: EMNLP 2020. Association for computational linguistics, Online, pp 766\u2013781, https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.68","DOI":"10.18653\/v1\/2020.findings-emnlp.68"},{"key":"5217_CR12","doi-asserted-by":"publisher","unstructured":"Hu J, Yang Y, Chen C et\u00a0al (2020) SAS: dialogue state tracking via slot attention and slot information sharing. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for computational linguistics, Online, pp 6366\u20136375, https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.567","DOI":"10.18653\/v1\/2020.acl-main.567"},{"key":"5217_CR13","doi-asserted-by":"publisher","unstructured":"Chen L, Lv B, Wang C et\u00a0al (2020) Schema-guided multi-domain dialogue state tracking with graph attention neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7521\u20137528, https:\/\/doi.org\/10.1609\/aaai.v34i05.6250","DOI":"10.1609\/aaai.v34i05.6250"},{"key":"5217_CR14","doi-asserted-by":"publisher","unstructured":"Feng Y, Lipani A, Ye F et\u00a0al (2022) Dynamic schema graph fusion network for multi-domain dialogue state tracking. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers). Association for computational linguistics, Dublin, Ireland, pp 115\u2013126, https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.10","DOI":"10.18653\/v1\/2022.acl-long.10"},{"key":"5217_CR15","doi-asserted-by":"publisher","unstructured":"Ye F, Manotumruksa J, Zhang Q et\u00a0al (2021) Slot self-attentive dialogue state tracking. In: Proceedings of the Web Conference 2021. Association for computing machinery, New York, NY, USA, WWW\u201921, pp 1598\u20131608, https:\/\/doi.org\/10.1145\/3442381.3449939","DOI":"10.1145\/3442381.3449939"},{"key":"5217_CR16","doi-asserted-by":"publisher","unstructured":"Budzianowski P, Wen TH, Tseng BH et\u00a0al (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. Association for Computational Linguistics, Brussels, Belgium, pp 5016\u20135026, https:\/\/doi.org\/10.18653\/v1\/D18-1547","DOI":"10.18653\/v1\/D18-1547"},{"key":"5217_CR17","unstructured":"Eric M, Goel R, Paul S et\u00a0al (2020) MultiWOZ 2.1: A consolidated multi-domain dialogue dataset with state corrections and state tracking baselines. In: Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp 422\u2013428"},{"key":"5217_CR18","unstructured":"Ye F, Manotumruksa J, Yilmaz E (2022) MultiWOZ 2.4: A multi-domain task-oriented dialogue dataset with essential annotation corrections to improve state tracking evaluation. In: Proceedings of the 23rd annual meeting of the special interest group on discourse and dialogue. Association for computational linguistics, Edinburgh, UK, pp 351\u2013360"},{"issue":"4","key":"5217_CR19","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. Comput Speech Language 24(4):562\u2013588. https:\/\/doi.org\/10.1016\/j.csl.2009.07.003","journal-title":"Comput Speech Language"},{"key":"5217_CR20","doi-asserted-by":"publisher","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). Association for computational linguistics, Philadelphia, PA, U.S.A., pp 292\u2013299, https:\/\/doi.org\/10.3115\/v1\/W14-4340","DOI":"10.3115\/v1\/W14-4340"},{"key":"5217_CR21","doi-asserted-by":"publisher","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). Association for computational linguistics, Philadelphia, PA, U.S.A., pp 282\u2013291, https:\/\/doi.org\/10.3115\/v1\/W14-4339","DOI":"10.3115\/v1\/W14-4339"},{"key":"5217_CR22","doi-asserted-by":"crossref","unstructured":"Wen TH, Vandyke D, Mrk\u0161i\u0107 N et\u00a0al (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: Volume 1, Long Papers. Association for computational linguistics, Valencia, Spain, pp 438\u2013449","DOI":"10.18653\/v1\/E17-1042"},{"key":"5217_CR23","doi-asserted-by":"publisher","unstructured":"Gao S, Sethi A, Agarwal S et\u00a0al (2019) Dialog state tracking: A neural reading comprehension approach. In: Proceedings of the 20th annual SIGdial meeting on discourse and dialogue. Association for computational linguistics, Stockholm, Sweden, pp 264\u2013273, https:\/\/doi.org\/10.18653\/v1\/W19-5932","DOI":"10.18653\/v1\/W19-5932"},{"key":"5217_CR24","doi-asserted-by":"publisher","unstructured":"Gao S, Agarwal S, Jin D et\u00a0al (2020) From machine reading comprehension to dialogue state tracking: Bridging the gap. In: Proceedings of the 2nd workshop on natural language processing for conversational AI. Association for computational linguistics, Online, pp 79\u201389, https:\/\/doi.org\/10.18653\/v1\/2020.nlp4convai-1.10","DOI":"10.18653\/v1\/2020.nlp4convai-1.10"},{"key":"5217_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-82136-4_44","author":"Y He","year":"2021","unstructured":"He Y, Tang Y (2021) A neural language understanding for dialogue state tracking. Knowl Sci Eng Manag. https:\/\/doi.org\/10.1007\/978-3-030-82136-4_44","journal-title":"Knowl Sci Eng Manag"},{"key":"5217_CR26","doi-asserted-by":"publisher","unstructured":"Rastogi P, Gupta A, Chen T et\u00a0al (2019) Scaling multi-domain dialogue state tracking via query reformulation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). Association for computational linguistics, Minneapolis, Minnesota, pp 97\u2013105, https:\/\/doi.org\/10.18653\/v1\/N19-2013","DOI":"10.18653\/v1\/N19-2013"},{"key":"5217_CR27","doi-asserted-by":"publisher","unstructured":"Ren L, Ni J, McAuley J (2019) Scalable and accurate dialogue state tracking via hierarchical sequence generation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for computational linguistics, Hong Kong, China, pp 1876\u20131885, https:\/\/doi.org\/10.18653\/v1\/D19-1196","DOI":"10.18653\/v1\/D19-1196"},{"key":"5217_CR28","doi-asserted-by":"publisher","unstructured":"Ren L, Xie K, Chen L et\u00a0al (2018) Towards universal dialogue state tracking. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for computational linguistics, Brussels, Belgium, pp 2780\u20132786, https:\/\/doi.org\/10.18653\/v1\/D18-1299","DOI":"10.18653\/v1\/D18-1299"},{"key":"5217_CR29","doi-asserted-by":"publisher","unstructured":"Zhong V, Xiong C, Socher R (2018) Global-locally self-attentive encoder for dialogue state tracking. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for computational linguistics, Melbourne, Australia, pp 1458\u20131467, https:\/\/doi.org\/10.18653\/v1\/P18-1135","DOI":"10.18653\/v1\/P18-1135"},{"key":"5217_CR30","unstructured":"Mou X, Sigouin B, Steenstra I et\u00a0al (2020) Multimodal dialogue state tracking by QA approach with data augmentation. Preprint at arXiv: 2007.09903"},{"key":"5217_CR31","doi-asserted-by":"publisher","unstructured":"Ouyang Y, Chen M, Dai X et\u00a0al (2020) Dialogue state tracking with explicit slot connection modeling. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for computational linguistics, Online, pp 34\u201340, https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.5","DOI":"10.18653\/v1\/2020.acl-main.5"},{"key":"5217_CR32","doi-asserted-by":"publisher","unstructured":"Wu CS, Madotto A, Hosseini-Asl E et\u00a0al (2019) Transferable multi-domain state generator for task-oriented dialogue systems. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for computational linguistics, Florence, Italy, pp 808\u2013819, https:\/\/doi.org\/10.18653\/v1\/P19-1078","DOI":"10.18653\/v1\/P19-1078"},{"key":"5217_CR33","unstructured":"Yang P, Huang H, Mao XL (2020) Context-sensitive generation network for handing unknown slot values in dialogue state tracking. Preprint at arXiv: 2005.03923"},{"key":"5217_CR34","doi-asserted-by":"publisher","unstructured":"Feng Y, Wang Y, Li H (2021) A sequence-to-sequence approach to dialogue state tracking. 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). Association for computational linguistics, Online, pp 1714\u20131725, https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.135","DOI":"10.18653\/v1\/2021.acl-long.135"},{"key":"5217_CR35","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, Lee K et\u00a0al (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for computational linguistics, Minneapolis, Minnesota, pp 4171\u20134186, https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"5217_CR36","doi-asserted-by":"publisher","unstructured":"Zhu Q, Li B, Mi F et\u00a0al (2022) Continual prompt tuning for dialog state tracking. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers). Association for computational linguistics, Dublin, Ireland, pp 1124\u20131137, https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.80","DOI":"10.18653\/v1\/2022.acl-long.80"},{"key":"5217_CR37","doi-asserted-by":"publisher","unstructured":"Lin Z, Madotto A, Winata GI et\u00a0al (2020) MinTL: Minimalist transfer learning for task-oriented dialogue systems. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for computational linguistics, Online, pp 3391\u20133405, https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.273","DOI":"10.18653\/v1\/2020.emnlp-main.273"},{"key":"5217_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3183081","author":"Y Wang","year":"2022","unstructured":"Wang Y, He T, Mei J et al (2022) A stack-propagation framework with slot filling for multi-domain dialogue state tracking. IEEE Trans Neur Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3183081","journal-title":"IEEE Trans Neur Netw Learn Syst"},{"key":"5217_CR39","doi-asserted-by":"publisher","unstructured":"Sun H, Bao J, Wu Y et\u00a0al (2022) BORT: Back and denoising reconstruction for end-to-end task-oriented dialog. In: Findings of the association for computational linguistics: NAACL 2022. Association for computational linguistics, Seattle, United States, pp 2156\u20132170, https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.166","DOI":"10.18653\/v1\/2022.findings-naacl.166"},{"key":"5217_CR40","unstructured":"Vaswani A, Shazeer N, Parmar N et\u00a0al (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, NIPS\u201917, pp 6000\u20136010"},{"key":"5217_CR41","doi-asserted-by":"publisher","unstructured":"Shen T, Wang X (2020) Multi-domain dialogue state tracking with hierarchical task graph. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138, https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9206790","DOI":"10.1109\/IJCNN48605.2020.9206790"},{"key":"5217_CR42","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et\u00a0al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5217_CR43","unstructured":"Lei Ba J, Kiros JR, Hinton GE (2016) Layer normalization. Preprint at arXiv: 1607.06450"},{"key":"5217_CR44","unstructured":"Chen Z, Chen L, Xu Z et\u00a0al (2020) CREDIT: coarse-to-fine sequence generation for dialogue state tracking. Preprint at arXiv: 2009.10435"},{"key":"5217_CR45","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et\u00a0al (2018) Graph attention networks. In: International Conference on Learning Representations"},{"key":"5217_CR46","doi-asserted-by":"publisher","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: International Conference on Learning Representations, https:\/\/doi.org\/10.48550\/arXiv.1711.05101","DOI":"10.48550\/arXiv.1711.05101"},{"issue":"1","key":"5217_CR47","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Machine Learn Res 15(1):1929\u20131958","journal-title":"J Machine Learn Res"},{"key":"5217_CR48","doi-asserted-by":"publisher","unstructured":"Bowman SR, Vilnis L, Vinyals O et\u00a0al (2016) Generating sentences from a continuous space. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Association for computational linguistics (ACL), Berlin, Germany, pp 10\u201321, https:\/\/doi.org\/10.18653\/v1\/K16-1002","DOI":"10.18653\/v1\/K16-1002"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05217-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05217-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05217-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T03:31:55Z","timestamp":1702438315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05217-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,16]]},"references-count":48,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["5217"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05217-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,16]]},"assertion":[{"value":"19 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}