{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:55:52Z","timestamp":1743123352696,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030594121"},{"type":"electronic","value":"9783030594138"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59413-8_15","type":"book-chapter","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T16:54:52Z","timestamp":1600707292000},"page":"175-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Survey on Modularization of Chatbot Conversational Systems"],"prefix":"10.1007","author":[{"given":"Xinzhi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Shiyulong","family":"He","sequence":"additional","affiliation":[]},{"given":"Zhenfei","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Buchholz, S., Marsi, E.: CoNLL-X shared task on multilingual dependency parsing. In: Proceedings of the CoNLL 2006, pp. 149\u2013164 (2006)","DOI":"10.3115\/1596276.1596305"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., Manning, C.D.: A fast and accurate dependency parser using neural networks. In: EMNLP, pp. 740\u2013750 (2014)","DOI":"10.3115\/v1\/D14-1082"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Weiss, D., Alberti, C., Collins, M., Petrov, S.: Structured training for neural network transition-based parsing. In: ACL (1), pp. 323\u2013333 (2015)","DOI":"10.3115\/v1\/P15-1032"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Ma, M., Huang, L., Zhou, B., Xiang, B.: Dependency-based convolutional neural networks for sentence embedding. In: ACL (2), pp. 174\u2013179 (2015)","DOI":"10.3115\/v1\/P15-2029"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746\u20131751 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Ji, T., Wu, Y., Lan, M.: Graph-based dependency parsing with graph neural networks. In: ACL (1), pp. 2475\u20132485 (2019)","DOI":"10.18653\/v1\/P19-1237"},{"key":"15_CR7","unstructured":"Kunsner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: ICML, pp. 957\u2013966 (2015)"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR (1), pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"15_CR9","unstructured":"Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: NIPS, pp. 2042\u20132050 (2014)"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Huang, P.S., He, X., Gao, J., et al.: Learning deep structured semantic models for web search using clickthrough data. In: CIKM, pp. 2333\u20132338 (2013)","DOI":"10.1145\/2505515.2505665"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: A latent semantic model with convolutional-pooling structure for information retrieval. In: CIKM, pp. 101\u2013110 (2014)","DOI":"10.1145\/2661829.2661935"},{"key":"15_CR12","unstructured":"Palangi, H., et al.: Semantic modelling with long-short-term memory for information retrieval. CoRR abs\/1412.6629 (2014)"},{"key":"15_CR13","unstructured":"Qiu, X., Huang, X.: Convolutional neural tensor network architecture for community-based question answering. In: IJCAI, pp. 1305\u20131311 (2015)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Yin, W., Sch\u00fctze, H.: MultiGranCNN: an architecture for general matching of text chunks on multiple levels of granularity. In: ACL (1), pp. 63\u201373 (2015)","DOI":"10.3115\/v1\/P15-1007"},{"key":"15_CR15","unstructured":"Yu, Z., Liu, G.: Sliced recurrent neural networks. In: COLING 2018, pp. 2953\u20132964 (2018)"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Pang, L., Lan, Y., Guo, J., Xu, J., Wan, S., Cheng, X.: Text matching as image recognition. In: AAAI, pp. 2793\u20132799 (2016)","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhu, X., Ling, Z.-H., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: ACL (1), pp. 1657\u20131668 (2017)","DOI":"10.18653\/v1\/P17-1152"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. In: IJCAI, pp. 4144\u20134150 (2017)","DOI":"10.24963\/ijcai.2017\/579"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Yin, W., Sch\u00fcutze, H., Xiang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. In: TACL4, pp. 259\u2013272 (2016)","DOI":"10.1162\/tacl_a_00097"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Pang, L., Lan, Y., Guo, J., Xu, J., Xu, J., Cheng, X.: DeepRank: a new deep architecture for relevance ranking in information retrieval. In: CIKM, pp. 257\u2013266 (2017)","DOI":"10.1145\/3132847.3132914"},{"key":"15_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":"15_CR22","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 (1), pp. 496\u2013505 (2017)","DOI":"10.18653\/v1\/P17-1046"},{"key":"15_CR23","unstructured":"Zhang, Z., Li, J., Zhu, P., Zhao, H., Liu, G.: Modeling multi-turn conversation with deep utterance aggregation. In: COLING, pp. 3740\u20133752 (2018)"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: Multi-turn response selection for chatbots with deep attention matching network. In: ACL (1), pp. 1118\u20131127 (2018)","DOI":"10.18653\/v1\/P18-1103"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Tao, C., Wu, W., Xu, C., Hu, W., Zhao, D., Yan, R: Multi-representation fusion network for multi-turn response selection in retrieval-based chatbots. In: WSDM, pp. 267\u2013275 (2019)","DOI":"10.1145\/3289600.3290985"},{"key":"15_CR26","unstructured":"Socher, R., Perelygin, A., Wu, J.Y., Chuang, J.: Recursive deep models for semantic compositionality over a sentiment Treebank. In: EMNLP, pp. 1631\u20131642 (2013)"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: ACL, pp. 1556\u20131566 (2015)","DOI":"10.3115\/v1\/P15-1150"},{"key":"15_CR28","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: COLING, pp. 3298\u20133307 (2016)"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: ACL, pp. 655\u2013665 (2014)","DOI":"10.3115\/v1\/P14-1062"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Conneau, A., Schwenk, H., Barrault, L., LeCun, Y.: Very deep convolutional networks for text classification. In: EACL, pp. 1107\u20131116 (2016)","DOI":"10.18653\/v1\/E17-1104"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. In: ACL (1), pp. 2514\u20132523 (2018)","DOI":"10.18653\/v1\/P18-1234"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Wang, B., Liu, K., Zhao, J.: Inner attention based recurrent neural networks for answer selection. In: ACL (1) (2016)","DOI":"10.18653\/v1\/P16-1122"},{"key":"15_CR33","doi-asserted-by":"crossref","unstructured":"He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In: ACL (1), pp. 504\u2013515 (2019)","DOI":"10.18653\/v1\/P19-1048"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Xu, J., Sun, X.: Dependency-based gated recursive neural network for Chinese word segmentation. In: ACL (2) (2016)","DOI":"10.18653\/v1\/P16-2092"},{"issue":"2","key":"15_CR35","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1023\/A:1026543900054","volume":"40","author":"Y Rubner","year":"2000","unstructured":"Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover\u2019s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99\u2013121 (2000)","journal-title":"Int. J. Comput. Vis."},{"key":"15_CR36","unstructured":"Norouzi, M., Fleet, D.J., Salakhutdinov, R.: Hamming distance metric learning. In: NIPS, pp. 1070\u20131078 (2012)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications. DASFAA 2020 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59413-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T07:30:59Z","timestamp":1723620659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59413-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030594121","9783030594138"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59413-8_15","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":"22 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"24 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/db.pknu.ac.kr\/dasfaa2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"487","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":"119","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":"23","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":"24% - 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":"3.11","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":"6.81","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15 demo papers and 4 industrial papers","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)"}}]}}