{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:47:37Z","timestamp":1743014857922,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030821463"},{"type":"electronic","value":"9783030821470"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-82147-0_45","type":"book-chapter","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:26:36Z","timestamp":1628292396000},"page":"550-561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sentence Matching with Deep Self-attention and Co-attention Features"],"prefix":"10.1007","author":[{"given":"Zhipeng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Danfeng","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"45_CR1","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference (2015)","DOI":"10.18653\/v1\/D15-1075"},{"key":"45_CR2","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences (2017)","DOI":"10.24963\/ijcai.2017\/579"},{"key":"45_CR3","unstructured":"Gong, Y., Luo, H., Zhang, J.: Natural language inference over interaction space (2017)"},{"key":"45_CR4","doi-asserted-by":"crossref","unstructured":"Huang, P.-S., et al.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2333\u20132338 (2013)","DOI":"10.1145\/2505515.2505665"},{"key":"45_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., Inkpen, D.: Enhanced lstm for natural language inference (2016)","DOI":"10.18653\/v1\/P17-1152"},{"key":"45_CR6","doi-asserted-by":"crossref","unstructured":"Liu, P., Qiu, X., Chen, J., Huang, X.-J.: Deep fusion lstms for text semantic matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1034\u20131043 (2016)","DOI":"10.18653\/v1\/P16-1098"},{"key":"45_CR7","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., T\u00e4ckstr\u00f6m, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference (2016)","DOI":"10.18653\/v1\/D16-1244"},{"key":"45_CR9","doi-asserted-by":"crossref","unstructured":"Kim, S., Kang, I., Kwak, N.: Semantic sentence matching with densely-connected recurrent and co-attentive information. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6586\u20136593 (2019)","DOI":"10.1609\/aaai.v33i01.33016586"},{"key":"45_CR10","doi-asserted-by":"crossref","unstructured":"Tan, C., Wei, F., Wang, W., Lv, W., Zhou, M.: Multiway attention networks for modeling sentence pairs. In: IJCAI, pp. 4411\u20134417 (2018)","DOI":"10.24963\/ijcai.2018\/613"},{"key":"45_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"45_CR12","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization (2016)"},{"issue":"4","key":"45_CR13","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MNET.2018.1700407","volume":"32","author":"K Gai","year":"2018","unstructured":"Gai, K., Qiu, M.: Reinforcement learning-based content-centric services in mobile sensing. IEEE Network 32(4), 34\u201339 (2018)","journal-title":"IEEE Network"},{"issue":"3","key":"45_CR14","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1109\/TBDATA.2016.2597149","volume":"4","author":"W Dai","year":"2016","unstructured":"Dai, W., Qiu, L., Wu, A., Qiu, M.: Cloud infrastructure resource allocation for big data applications. IEEE Trans. Big Data 4(3), 313\u2013324 (2016)","journal-title":"IEEE Trans. Big Data"},{"issue":"2","key":"45_CR15","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/TSUSC.2017.2723954","volume":"3","author":"K Gai","year":"2017","unstructured":"Gai, K., Qiu, M., Zhao, H., Sun, X.: Resource management in sustainable cyber-physical systems using heterogeneous cloud computing. IEEE Trans. Sustainable Comput. 3(2), 60\u201372 (2017)","journal-title":"IEEE Trans. Sustainable Comput."},{"key":"45_CR16","unstructured":"Romano, L., Kouylekov, M., Szpektor, I., Dagan, I., Lavelli, A.: Investigating a generic paraphrase-based approach for relation extraction. In: 11th Conference of the European Chapter of the Association for Computational Linguistics (2006)"},{"key":"45_CR17","doi-asserted-by":"crossref","unstructured":"Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data (2017)","DOI":"10.18653\/v1\/D17-1070"},{"key":"45_CR18","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543. 2014","DOI":"10.3115\/v1\/D14-1162"},{"key":"45_CR19","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"45_CR20","unstructured":"Wang, S., Jiang, J.: A compare-aggregate model for matching text sequence. arXiv preprint arXiv:1611.01747 2016"},{"key":"45_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"45_CR22","doi-asserted-by":"crossref","unstructured":"Yang, R., Zhang, J., Gao, X., Ji, F., Chen, H.: Simple and effective text matching with richer alignment features (2019)","DOI":"10.18653\/v1\/P19-1465"},{"key":"45_CR23","doi-asserted-by":"crossref","unstructured":"Tay, Y., Luu, A.T., Hui, S.C.: Hermitian co-attention networks for text matching in asymmetrical domains. In: IJCAI, pp. 4425\u20134431 (2018)","DOI":"10.24963\/ijcai.2018\/615"},{"key":"45_CR24","doi-asserted-by":"crossref","unstructured":"Tay, Y., Tuan, L.A., Hui, S.C.: Compare, compress and propagate: Enhancing neural architectures with alignment factorization for natural language inference (2017)","DOI":"10.18653\/v1\/D18-1185"},{"key":"45_CR25","doi-asserted-by":"crossref","unstructured":"Tay, Y., Tuan, L.A., Hui, S.C.: Co-stack residual affinity networks with multi-level attention refinement for matching text sequences (2018)","DOI":"10.18653\/v1\/D18-1479"},{"key":"45_CR26","doi-asserted-by":"crossref","unstructured":"Khot, T., Sabharwal, A., Clark, P.: SciTaiL: a textual entailment dataset from science question answering. In: AAAI, pp. 41\u201342 (2018)","DOI":"10.1609\/aaai.v32i1.12022"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82147-0_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T21:21:05Z","timestamp":1673040065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82147-0_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030821463","9783030821470"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82147-0_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/ksem21\/index.html","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"492","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":"164","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":"0","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":"33% - 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","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":"10","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)"}}]}}