{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:05:27Z","timestamp":1743026727887,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030638290"},{"type":"electronic","value":"9783030638306"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63830-6_3","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T10:08:18Z","timestamp":1605694098000},"page":"24-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Interactive Matching Network for Multi-turn Response Selection in Retrieval-Based Chatbots"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7291-6104","authenticated-orcid":false,"given":"Ting","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7118-6314","authenticated-orcid":false,"given":"Ruifang","family":"He","sequence":"additional","affiliation":[]},{"given":"Longbiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9237-4821","authenticated-orcid":false,"given":"Jiangwu","family":"Dang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"3_CR1","unstructured":"Ba, J., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv abs\/1607.06450 (2016)"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition pp. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR3","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv abs\/1502.03167 (2015)"},{"key":"3_CR4","unstructured":"Kadlec, R., Schmid, M., Kleindienst, J.: Improved deep learning baselines for ubuntu corpus dialogs. arXiv abs\/1510.03753 (2015)"},{"key":"3_CR5","unstructured":"Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2014)"},{"key":"3_CR6","unstructured":"Kyle, S., Lili, Y., Christopher, F., WohlwendJeremy, Tao, L.: Building a production model for retrieval-based chatbots. In: Proceedings of the First Workshop on NLP for Conversational AI, pp. 32\u201341 (2019)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Lowe, R., Pow, N., Serban, I., Pineau, J.: The Ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. pp. 285\u2013294 (2015)","DOI":"10.18653\/v1\/W15-4640"},{"key":"3_CR8","unstructured":"Lu, Z., Li, H.: A deep architecture for matching short texts. In: International Conference on Neural Information Processing Systems, pp. 1367\u20131375 (2013)"},{"key":"3_CR9","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Schegloff, E.A., Sacks, H.: Opening up closings. J. Int. Assoc. Semiotic Stud. 8(4), (1973)","DOI":"10.1515\/semi.1973.8.4.289"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Tao, C., Wu, W., Xu, C., Hu, W., Zhao, D., Yan, R.: One time of interaction may not be enough:go deep with an interaction-over-interaction network for response selection in dialogues. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1\u201311 (2019)","DOI":"10.18653\/v1\/P19-1001"},{"key":"3_CR12","unstructured":"Vaswani, A., et al.: Attention is all you need. In: International Conference on Neural Information Processing Systems (2017)"},{"key":"3_CR13","unstructured":"Vinyals, O., Le, Q.V.: A neural conversational model. arXiv abs\/1506.05869 (2015)"},{"key":"3_CR14","unstructured":"Wan, S., Lan, Y., Xu, J., Guo, J., Pang, L., Cheng, X.: Match-srnn: Modeling the recursive matching structure with spatial RNN. In: IJCAI (2016)"},{"key":"3_CR15","unstructured":"Wang, M., Lu, Z., Li, H., Liu, Q.: Syntax-based deep matching of short texts. In: IJCAI, pp. 1354\u20131361 (2015)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Wang, S., Jiang, J.: Learning natural language inference with LSTM. In: NAACL, pp. 1442\u20131451 (2016)","DOI":"10.18653\/v1\/N16-1170"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential matching network: a new architecture for multi-turn response selection in retrieval-based chatbots. In: ACL, pp. 496\u2013505 (2017)","DOI":"10.18653\/v1\/P17-1046"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Yan, R., Song, Y., Wu, H.: Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In: SIGIR 2016 (2016)","DOI":"10.1145\/2911451.2911542"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, H., Lan, Y., Pang, L., Guo, J., Cheng, X.: ReCoSa: detecting the relevant contexts with self-attention for multi-turn dialogue generation. In: ACL, pp. 3721\u20133730 (2019)","DOI":"10.18653\/v1\/P19-1362"},{"key":"3_CR20","unstructured":"Zhang, Z., Li, J., Zhu, P., Zhao, H., Liu, G.: Modeling multi-turn conversation with deep utterance aggregation. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 3740\u20133752 (2018)"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: Multi-view response selection for human-computer conversation. In: EMNLP, pp. 372\u2013381 (2016)","DOI":"10.18653\/v1\/D16-1036"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: Multi-turn response selection for chatbots with deep attention matching network. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1118\u20131127 (2018)","DOI":"10.18653\/v1\/P18-1103"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63830-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T14:05:44Z","timestamp":1709820344000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63830-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030638290","9783030638306"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63830-6_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangkok","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apnns.org\/ICONIP2020","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"618","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":"187","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":"189","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":"30% - 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":"3.18","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":"3.68","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":"Due to COVID-19 pandemic the conference was held virtually.","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)"}}]}}