{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:23:26Z","timestamp":1763018606883,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031416811"},{"type":"electronic","value":"9783031416828"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-41682-8_11","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:02:59Z","timestamp":1692342179000},"page":"166-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Topic Shift Detection in\u00a0Chinese Dialogues: Corpus and\u00a0Benchmark"],"prefix":"10.1007","author":[{"given":"Jiangyi","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaxin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomin","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peifeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Dai, S., Wang, G., Park, S., Lee, S.: Dialogue response generation via contrastive latent representation learning. In: Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pp. 189\u2013197 (2021)","DOI":"10.18653\/v1\/2021.nlp4convai-1.18"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Dadgraph: a discourse-aware dialogue graph neural network for multiparty dialogue machine reading comprehension. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533364"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhao, H.: Self-and pseudo-self-supervised prediction of speaker and key-utterance for multi-party dialogue reading comprehension. Find. Assoc. Comput. Linguist. EMNLP 2021, 2053\u20132063 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.176"},{"key":"11_CR4","unstructured":"Ghandeharioun, A., et al.: Approximating interactive human evaluation with self-play for open-domain dialog systems. Adv. Neural Inf. Process. Syst. 32, 13658\u201313669 (2019)"},{"key":"11_CR5","unstructured":"Einolghozati, A., Gupta, S., Mohit, M., Shah, R.: Improving robustness of task oriented dialog systems. arXiv preprint arXiv:1911.05153 (2019)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Liu, B., Tur, G., Hakkani-Tur, D., Shah, P., Heck, L.: Dialogue learning with human teaching and feedback in end-to-end trainable task-oriented dialogue systems. In: Proceedings of NAACL-HLT, pp. 2060\u20132069 (2018)","DOI":"10.18653\/v1\/N18-1187"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Xie, H., Liu, Z., Xiong, C., Liu, Z., Copestake, A.: Tiage: a benchmark for topic-shift aware dialog modeling. In: Findings of the Association for Computational Linguistics: EMNLP, vol. 2021, pp. 1684\u20131690 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.145"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhao, H., Zhang, Z.: Topic-aware multi-turn dialogue modeling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 14176\u201314184 (2021)","DOI":"10.1609\/aaai.v35i16.17668"},{"issue":"140","key":"11_CR9","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Hint-based training for non-autoregressive machine translation. 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. 5708\u20135713 (2019)","DOI":"10.18653\/v1\/D19-1573"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2204\u20132213 (2018)","DOI":"10.18653\/v1\/P18-1205"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Budzianowski, P., et al: Multiwoz-a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 5016\u20135026 (2018)","DOI":"10.18653\/v1\/D18-1547"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Eric, M., Krishnan, L., Charette, F., Manning, C.D.: Key-value retrieval networks for task-oriented dialogue. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pp. 37\u201349 (2017)","DOI":"10.18653\/v1\/W17-5506"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Eisenstein, J., Barzilay, R.: Bayesian unsupervised topic segmentation. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 334\u2013343 (2008)","DOI":"10.3115\/1613715.1613760"},{"key":"11_CR15","unstructured":"Du, L., Buntine, W., Johnson, M.: Topic segmentation with a structured topic model. In: Proceedings of the 2013 conference of the North American Chapter of the Association for Computational Linguistics: Human language technologies, pp. 190\u2013200 (2013)"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Koshorek, O., Cohen, A., Mor, N., Rotman, M., Berant, J.: Text segmentation as a supervised learning task. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 469\u2013473 (2018)","DOI":"10.18653\/v1\/N18-2075"},{"key":"11_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/978-3-319-76941-7_14","volume-title":"Advances in Information Retrieval","author":"Pinkesh Badjatiya","year":"2018","unstructured":"Badjatiya, Pinkesh, Kurisinkel, Litton J.., Gupta, Manish, Varma, Vasudeva: Attention-based neural text segmentation. In: Pasi, Gabriella, Piwowarski, Benjamin, Azzopardi, Leif, Hanbury, Allan (eds.) ECIR 2018. LNCS, vol. 10772, pp. 180\u2013193. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76941-7_14"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Arnold, S., Schneider R., Cudr\u00e9-Mauroux, P., Gers, F.A.,Alexander L\u00f6ser. Sector: A neural model for coherent topic segmentation and classification. Trans. Assoc. Comput. Linguist 7, 169\u2013184, 2019","DOI":"10.1162\/tacl_a_00261"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Yingcheng Sun and Kenneth Loparo. Topic shift detection in online discussions using structural context. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), volume 1, pages 948\u2013949. IEEE, 2019","DOI":"10.1109\/COMPSAC.2019.00155"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"14006","DOI":"10.1609\/aaai.v35i16.17649","volume":"35","author":"X Wang","year":"2021","unstructured":"Wang, X., Li, C., Zhao, J., Dong, Yu.: Naturalconv: A chinese dialogue dataset towards multi-turn topic-driven conversation. In Proceedings of the AAAI Conference on Artificial Intelligence 35, 14006\u201314014 (2021)","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"11_CR21","unstructured":"Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. Proactive human-machine conversation with explicit conversation goal. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3794\u20133804, 2019"},{"key":"11_CR22","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Shaoxiong Feng, Xuancheng Ren, Hongshen Chen, Bin Sun, Kan Li, and Xu Sun. Regularizing dialogue generation by imitating implicit scenarios. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6592\u20136604, 2020","DOI":"10.18653\/v1\/2020.emnlp-main.534"},{"key":"11_CR24","doi-asserted-by":"publisher","first-page":"11002","DOI":"10.1609\/aaai.v36i10.21348","volume":"36","author":"S Li","year":"2022","unstructured":"Li, S., Yan, H., Qiu, X.: Contrast and generation make bart a good dialogue emotion recognizer. In Proceedings of the AAAI Conference on Artificial Intelligence 36, 11002\u201311010 (2022)","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"11_CR25","unstructured":"Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. Supervised contrastive learning for pre-trained language model fine-tuning. In International Conference on Learning Representations"},{"key":"11_CR26","unstructured":"Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. Dailydialog: A manually labelled multi-turn dialogue dataset. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 986\u2013995, 2017"},{"key":"11_CR27","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171\u20134186, 2019"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition - ICDAR 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41682-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T09:40:47Z","timestamp":1729935647000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41682-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416811","9783031416828"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41682-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"19 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Jos\u00e9, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdar2023.org\/","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":"316","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":"154","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":"49% - 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":"2.89","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":"1.50","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":"Number and type of other papers accepted : IJDAR track 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)"}}]}}