{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:20:49Z","timestamp":1743128449060,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030884826"},{"type":"electronic","value":"9783030884833"}],"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\/978-3-030-88483-3_29","type":"book-chapter","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T13:55:25Z","timestamp":1633874125000},"page":"365-376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSDF: A General Open-Domain Multi-skill Dialog Framework"],"prefix":"10.1007","author":[{"given":"Yu","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinshuo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baotian","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongfang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sichao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Bao, S., et al.: Plato-2: towards building an open-domain chatbot via curriculum learning. arXiv preprint arXiv:2006.16779 (2020)","DOI":"10.18653\/v1\/2021.findings-acl.222"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Cai, D., Wang, Y., Bi, W., Tu, Z., Liu, X., Shi, S.: Retrieval-guided dialogue response generation via a matching-to-generation framework. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1866\u20131875 (2019)","DOI":"10.18653\/v1\/D19-1195"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Y., Bi, W., Fang, M., Tao, D.: Pretrained language models for dialogue generation with multiple input sources. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp. 909\u2013917 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.81"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Q., et al.: Towards knowledge-based recommender dialog system. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1803\u20131813 (2019)","DOI":"10.18653\/v1\/D19-1189"},{"key":"29_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)"},{"key":"29_CR6","unstructured":"Kim, B., Ahn, J., Kim, G.: Sequential latent knowledge selection for knowledge-grounded dialogue. In: International Conference on Learning Representations (2019)"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Lian, R., Xie, M., Wang, F., Peng, J., Wu, H.: Learning to select knowledge for response generation in dialog systems. In: IJCAI International Joint Conference on Artificial Intelligence, p. 5081 (2019)","DOI":"10.24963\/ijcai.2019\/706"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: You impress me: dialogue generation via mutual persona perception. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1417\u20131427 (2020)","DOI":"10.18653\/v1\/2020.acl-main.131"},{"key":"29_CR10","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, H., Niu, Z.Y., Wu, H., Che, W., Liu, T.: Towards conversational recommendation over multi-type dialogs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1036\u20131049 (2020)","DOI":"10.18653\/v1\/2020.acl-main.98"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Moon, S., Shah, P., Kumar, A., Subba, R.: Opendialkg: explainable conversational reasoning with attention-based walks over knowledge graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 845\u2013854 (2019)","DOI":"10.18653\/v1\/P19-1081"},{"key":"29_CR13","unstructured":"Ni, J., Young, T., Pandelea, V., Xue, F., Adiga, V., Cambria, E.: Recent advances in deep learning-based dialogue systems. arXiv preprint arXiv:2105.04387 (2021)"},{"issue":"8","key":"29_CR14","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)","journal-title":"OpenAI blog"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1073\u20131083 (2017)","DOI":"10.18653\/v1\/P17-1099"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Serban, I.V., Sordoni, A., Bengio, Y., Courville, A., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.9883"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1577\u20131586 (2015)","DOI":"10.3115\/v1\/P15-1152"},{"key":"29_CR18","first-page":"3104","volume":"27","author":"I Sutskever","year":"2014","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural. Inf. Process. Syst. 27, 3104\u20133112 (2014)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ke, P., Zheng, Y., Huang, K., Jiang, Y., Zhu, X., Huang, M.: A large-scale Chinese short-text conversation dataset. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 91\u2013103. Springer (2020)","DOI":"10.1007\/978-3-030-60450-9_8"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Wu, W., Guo, Z., Zhou, X., Wu, H., Zhang, X., Lian, R., Wang, H.: Proactive human-machine conversation with explicit conversation goal. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3794\u20133804 (2019)","DOI":"10.18653\/v1\/P19-1369"},{"key":"29_CR21","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: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 496\u2013505 (2017)","DOI":"10.18653\/v1\/P17-1046"},{"key":"29_CR22","unstructured":"Xu, M., et al.: A neural topical expansion framework for unstructured persona-oriented dialogue generation. arXiv preprint arXiv:2002.02153 (2020)"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Zeng, G., et al.: Meddialog: a large-scale medical dialogue dataset. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9241\u20139250 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.743"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Dialogpt: large-scale generative pre-training for conversational response generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 270\u2013278 (2020)","DOI":"10.18653\/v1\/2020.acl-demos.30"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, H., Huang, M., Zhang, T., Zhu, X., Liu, B.: Emotional chatting machine: Emotional conversation generation with internal and external memory. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11325"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zheng, C., Huang, K., Huang, M., Zhu, X.: Kdconv: A chinese multi-domain dialogue dataset towards multi-turn knowledge-driven conversation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 7098\u20137108 (2020)","DOI":"10.18653\/v1\/2020.acl-main.635"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhao, W.X., Bian, S., Zhou, Y., Wen, J.R., Yu, J.: Improving conversational recommender systems via knowledge graph based semantic fusion. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1006\u20131014 (2020)","DOI":"10.1145\/3394486.3403143"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhou, Y., Zhao, W.X., Wang, X., Wen, J.R.: Towards topic-guided conversational recommender system. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 4128\u20134139 (2020)","DOI":"10.18653\/v1\/2020.coling-main.365"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88483-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:34:58Z","timestamp":1710257698000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88483-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030884826","9783030884833"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88483-3_29","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":"6 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","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":"13 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2021\/","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":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"446","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":"66","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":"15% - 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":"1.5","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":"23 poster papers and 27 workshop papers are also included.","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)"}}]}}