{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:45:24Z","timestamp":1742985924666,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930486"},{"type":"electronic","value":"9783030930493"}],"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\/978-3-030-93049-3_23","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T06:00:24Z","timestamp":1641016824000},"page":"273-284","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Judging Medical Q&A Alignments in\u00a0Multiple Aspects"],"prefix":"10.1007","author":[{"given":"Pengda","family":"Si","sequence":"first","affiliation":[]},{"given":"Qiang","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yiru","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yujiu","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Volume 1: Long Papers, pp. 1657\u20131668. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/P17-1152"},{"key":"23_CR2","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, NeurIPS2016, pp. 2172\u20132180 (2016)"},{"key":"23_CR3","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. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, pp. 670\u2013680. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/D17-1070"},{"key":"23_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, no. 1 (2019)"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: Exploration and evaluation. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), pp. 663\u2013670. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.11330"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"He, H., Lin, J.J.: Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In: NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 937\u2013948. The Association for Computational Linguistics (2016)","DOI":"10.18653\/v1\/N16-1108"},{"issue":"8","key":"23_CR7","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Jawahar, G., Sagot, B., Seddah, D.: What does BERT learn about the structure of language? In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Volume 1: Long Papers, pp. 3651\u20133657. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1356"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"John, V., Mou, L., Bahuleyan, H., Vechtomova, O.: Disentangled representation learning for non-parallel text style transfer. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, Volume 1: Long Papers, pp. 424\u2013434. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1041"},{"key":"23_CR10","unstructured":"Lan, W., Xu, W.: Neural network models for paraphrase identification, semantic textual similarity, natural language inference, and question answering. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3890\u20133902 (2018)"},{"key":"23_CR11","unstructured":"Liu, Y., Sun, C., Lin, L., Wang, X.: Learning natural language inference using bidirectional lstm model and inner-attention. arXiv preprint arXiv:1605.09090 (2016)"},{"key":"23_CR12","unstructured":"Liu, Y., et al.: Roberta: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019)"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 6997\u20137005. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.740"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 2786\u20132792. AAAI Press (2016)","DOI":"10.1609\/aaai.v30i1.10350"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Nie, Y., Bansal, M.: Shortcut-stacked sentence encoders for multi-domain inference. In: Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, RepEval@EMNLP 2017, pp. 41\u201345. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/W17-5308"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Parikh, A., T\u00e4ckstr\u00f6m, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2249\u20132255 (2016)","DOI":"10.18653\/v1\/D16-1244"},{"key":"23_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-030-58545-7_19","volume-title":"Computer Vision","author":"T Park","year":"2020","unstructured":"Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 319\u2013345. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_19"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Rao, J., Liu, L., Tay, Y., Yang, H., Shi, P., Lin, J.: Bridging the gap between relevance matching and semantic matching for short text similarity modeling. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, EMNLP-IJCNLP 2019, pp. 5369\u20135380. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1540"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, EMNLP-IJCNLP 2019, pp. 3980\u20133990. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"23_CR20","unstructured":"Shen, T., Lei, T., Barzilay, R., Jaakkola, T.S.: Style transfer from non-parallel text by cross-alignment. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, NeurIPS2017, pp. 6830\u20136841 (2017)"},{"key":"23_CR21","unstructured":"Shonibare, O.: ASBERT: siamese and triplet network embedding for open question answering. CoRR abs\/2104.08558 (2021)"},{"key":"23_CR22","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J.G., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems, NeurIPS2019, pp. 5754\u20135764 (2019)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: Multi-view response selection for human-computer conversation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, pp. 372\u2013381. The Association for Computational Linguistics (2016)","DOI":"10.18653\/v1\/D16-1036"},{"key":"23_CR24","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, ACL 2018, Volume 1: Long Papers, pp. 1118\u20131127. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-1103"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93049-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T11:35:22Z","timestamp":1674300922000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93049-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930486","9783030930493"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93049-3_23","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":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"105","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":"34% - 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":"3.2","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":"5.3","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)"}}]}}