{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:08:39Z","timestamp":1743084519691,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442155"},{"type":"electronic","value":"9783031442162"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44216-2_26","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:02:58Z","timestamp":1695279778000},"page":"316-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Co-RGCN: A Bi-path GCN-Based Co-Regression Model for\u00a0Multi-intent Detection and\u00a0Slot Filling"],"prefix":"10.1007","author":[{"given":"Qingpeng","family":"Wen","sequence":"first","affiliation":[]},{"given":"Bi","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Pengfei","family":"Wei","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"8","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3547138","volume":"55","author":"H Weld","year":"2022","unstructured":"Weld, H., Huang, X., Long, S., et al.: A survey of joint intent detection and slot filling models in natural language understanding. ACM Comput. Surv. 55(8), 1\u201338 (2022)","journal-title":"ACM Comput. Surv."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Kim, S., D\u2019Haro, L.F., et al.: The fourth dialog state tracking challenge. Dialogues Soc. Robots: Enablements, Anal. Eval. 435\u2013449 (2017)","DOI":"10.1007\/978-981-10-2585-3_36"},{"key":"26_CR3","unstructured":"Haffner, P., Tur, G., Wright, J.H.: Optimizing SVMs for complex call classification. In: ICASSP (2003)"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., et al.: Recurrent convolutional neural networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29, no. 1 (2015)","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP 2014, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Raymond, C., Riccardi, G.: Generative and discriminative algorithms for spoken language understanding. In: Interspeech 2007\u20138th ISCA, pp. 1605\u20131608 (2007)","DOI":"10.21437\/Interspeech.2007-448"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Yao, K., Peng, B., Zhang, Y., et al.: Spoken language understanding using long short-term memory neural networks. In: SLT, pp. 189\u2013194 (2014)","DOI":"10.1109\/SLT.2014.7078572"},{"key":"26_CR8","unstructured":"Chen, Q., Zhuo, Z., Wang, W.: Bert for joint intent classification and slot filling[J]. arXiv preprint arXiv:1902.10909, 2019"},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"168849","DOI":"10.1109\/ACCESS.2019.2954766","volume":"7","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Zhang, Z., Chen, H., et al.: A joint learning framework with BERT for spoken language understanding[J]. IEEE Access 7, 168849\u2013168858 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"26_CR10","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.3233\/JIFS-211674","volume":"42","author":"P Wei","year":"2022","unstructured":"Wei, P., Zeng, B., Liao, W.: Joint intent detection and slot filling with wheel-graph attention networks. J. Intell. Fuzzy Syst. 42(3), 2409\u20132420 (2022)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Xu, P., Sarikaya, R.: Convolutional neural network based triangular CRF for joint intent detection and slot filling. In: ASRU, pp. 78\u201383 (2013)","DOI":"10.1109\/ASRU.2013.6707709"},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"11377","DOI":"10.1007\/s11042-016-3724-4","volume":"76","author":"B Kim","year":"2017","unstructured":"Kim, B., Ryu, S., Lee, G.G.: Two-stage multi-intent detection for spoken language understanding. Multimedia Tools Appl. 76, 11377\u201311390 (2017)","journal-title":"Multimedia Tools Appl."},{"key":"26_CR13","doi-asserted-by":"publisher","first-page":"16149","DOI":"10.1007\/s00521-020-04805-x","volume":"32","author":"P Ni","year":"2020","unstructured":"Ni, P., Li, Y., Li, G., et al.: Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interaction. Neural Comput. Appl. 32, 16149\u201316166 (2020)","journal-title":"Neural Comput. Appl."},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.neucom.2020.06.113","volume":"413","author":"H Tang","year":"2020","unstructured":"Tang, H., Ji, D., Zhou, Q.: End-to-end masked graph-based CRF for joint slot filling and intent detection. Neurocomputing 413, 348\u2013359 (2020)","journal-title":"Neurocomputing"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Qin, L., Xu, X., Che, W., et al.: AGIF: an adaptive graph-interactive framework for joint multiple intent detection and slot filling. In: Findings of the Association for Computational Linguistics: EMNLP, vol. 2020, pp. 1807\u20131816 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.163"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Qin, L., Wei, F., Xie, T., et al.: GL-GIN: fast and accurate non-autoregressive model for joint multiple intent detection and slot filling. In: Proceedings of the 59th ACL and the 11th IJCNLP, pp. 178\u2013188 (2021)","DOI":"10.18653\/v1\/2021.acl-long.15"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Gangadharaiah, R., Narayanaswamy, B.: Joint multiple intent detection and slot labeling for goal-oriented dialog. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 564\u2013569 (2019)","DOI":"10.18653\/v1\/N19-1055"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Goo, C.W., Gao, G., Hsu, Y.K., et al.: Slot-gated modeling for joint slot filling and intent prediction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 753\u2013757 (2018)","DOI":"10.18653\/v1\/N18-2118"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhou, P., Zou, Y.: Joint multiple intent detection and slot filling via self-distillation. In: ICASSP, pp. 7612\u20137616 (2022)","DOI":"10.1109\/ICASSP43922.2022.9747843"},{"key":"26_CR20","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, p. 30 (2017)"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Graves, A., Graves, A.: Long short-term memory. Supervised Sequence Labelling Recurrent Neural Netw. 37\u201345 (2012)","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"26_CR22","unstructured":"Liu, L., Jiang, H., He, P., et al.: On the variance of the adaptive learning rate and beyond. In: International Conference on Learning Representations"},{"key":"26_CR23","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Y., Shen, Y., Jin, H.: A Bi-model based RNN semantic frame parsing model for intent detection and slot filling. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 309\u2013314 (2018)","DOI":"10.18653\/v1\/N18-2050"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Qin, L., Che, W., et al.: A stack-propagation framework with token-level intent detection for spoken language understanding. In: EMNLP, pp. 2078\u20132087 (2019)","DOI":"10.18653\/v1\/D19-1214"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44216-2_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:06:54Z","timestamp":1695280014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44216-2_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442155","9783031442162"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44216-2_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}