{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:45:07Z","timestamp":1743075907870,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031208645"},{"type":"electronic","value":"9783031208652"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20865-2_17","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:29:12Z","timestamp":1667518152000},"page":"224-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bidirectional Macro-level Discourse Parser Based on\u00a0Oracle Selection"],"prefix":"10.1007","author":[{"given":"Longwang","family":"He","sequence":"first","affiliation":[]},{"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Xiaoyi","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Yaxin","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Weihao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Peifeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaomin","family":"Chu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"17_CR1","unstructured":"Liakata, M., Dobnik, S., Saha, S., Batchelor, C., Schuhmann, D.R.: A discourse-driven content model for summarising scientific articles evaluated in a complex question answering task. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 747\u2013757 (2013)"},{"key":"17_CR2","unstructured":"Meyer, T., Popescu-Belis, A.: Using sense-labeled discourse connectives for statistical machine translation. In: Proceedings of the EACL2012 Workshop on Hybrid Approaches to Machine Translation (HyTra), no. CONF (2012)"},{"key":"17_CR3","unstructured":"Cohan, A., Goharian, N.: Scientific article summarization using citation-context and article\u2019s discourse structure, arXiv preprint arXiv:1704.06619 (2017)"},{"key":"17_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/978-3-642-33876-2_12","volume-title":"Knowledge Engineering and Knowledge Management","author":"V Presutti","year":"2012","unstructured":"Presutti, V., Draicchio, F., Gangemi, A.: Knowledge extraction based on discourse representation theory and linguistic frames. In: ten Teije, A., V\u00f6lker, J., Handschuh, S., Stuckenschmidt, H., d\u2019Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 114\u2013129. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33876-2_12"},{"key":"17_CR5","unstructured":"Jiang, F., Xu, S., Chu, X., Li, P., Zhu, Q., Zhou, G.: Mcdtb: a macro-level chinese discourse treebank. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3493\u20133504 (2018)"},{"key":"17_CR6","unstructured":"Fan, Y., Jiang, F., Chu, X., Li, P., Zhu, Q.: Combining global and local information to recognize chinese macro discourse structure. In: Proceedings of the 19th Chinese National Conference on Computational Linguistics, pp. 183\u2013194 (2020)"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Liu, L., Lin, X., Joty, S., Han, S., Bing, L.: Hierarchical pointer net parsing, arXiv preprint arXiv:1908.11571 (2019)","DOI":"10.18653\/v1\/D19-1093"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Lin, X., Joty, S., Jwalapuram, P., Bari, M.S.: A unified linear-time framework for sentence-level discourse parsing, arXiv preprint arXiv:1905.05682 (2019)","DOI":"10.18653\/v1\/P19-1410"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Koto, F., Lau, J.H., Baldwin, T.: Top-down discourse parsing via sequence labelling, arXiv preprint arXiv:2102.02080 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.60"},{"key":"17_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/978-3-030-32233-5_60","volume-title":"Natural Language Processing and Chinese Computing","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., Chu, X., Li, P., Zhu, Q.: Constructing chinese macro discourse tree via multiple views and word pair similarity. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11838, pp. 773\u2013786. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32233-5_60"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, F., Chu, X., Li, P., Kong, F., Zhu, Q.: Chinese paragraph-level discourse parsing with global backward and local reverse reading. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 5749\u20135759 (2020)","DOI":"10.18653\/v1\/2020.coling-main.506"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, F., Fan, Y., Chu, X., Li, P., Zhu, Q., Kong, F.: Hierarchical macro discourse parsing based on topic segmentation. In: Proceedings of the Conference on Artificial Intelligence (AAAI), pp. 13152\u201313160 (2021)","DOI":"10.1609\/aaai.v35i14.17554"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Feng, V.W., Hirst, G.: A linear-time bottom-up discourse parser with constraints and post-editing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 511\u2013521 (2014)","DOI":"10.3115\/v1\/P14-1048"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Li, Q., Li, T., Chang, B.: Discourse parsing with attention-based hierarchical neural networks. In: EMNLP, pp. 362\u2013371 (2016)","DOI":"10.18653\/v1\/D16-1035"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Hung, S.S., Huang, H.H., Chen, H.H.: A complete shift-reduce chinese discourse parser with robust dynamic oracle. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 133\u2013138 (2020)","DOI":"10.18653\/v1\/2020.acl-main.13"},{"key":"17_CR16","series-title":"Communications in computer and information science","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-030-92307-5_9","volume-title":"Neural Information Processing","author":"J Zhou","year":"2021","unstructured":"Zhou, J., Jiang, F., Chu, X., Li, P., Zhu, Q.: More Than One-Hot: Chinese Macro Discourse Relation Recognition on Joint Relation Embedding. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. CCIS, vol. 1516, pp. 73\u201380. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92307-5_9"},{"key":"17_CR17","unstructured":"Chen, Q., Zhang, R., Zheng, Y., Mao, .: ual contrastive learning: Text classification via label-aware data augmentation, arXiv preprint arXiv:2201.08702,(2022)"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Carlson, L., Marcu, D., Okurowski, M.E. (2003). Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory. In: van Kuppevelt, J., Smith, R.W. (eds) Current and New Directions in Discourse and Dialogue. Text, Speech and Language Technology, vol 22. Springer, Dordrecht. https:\/\/doi.org\/10.1007\/978-94-010-0019-2_5","DOI":"10.1007\/978-94-010-0019-2_5"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Mabona, A., Rimell, L., Clark, S., Vlachos, A.: Neural generative rhetorical structure parsing, arXiv preprint arXiv:1909.11049 (2019)","DOI":"10.18653\/v1\/D19-1233"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Fried, D., Stern, M., Klein, D.: Improving neural parsing by disentangling model combination and reranking effects, arXiv preprint arXiv:1707.03058 (2017)","DOI":"10.18653\/v1\/P17-2025"},{"key":"17_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-32236-6_20","volume-title":"Natural Language Processing and Chinese Computing","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Tan, X., Kong, F., Zhou, G.: A recursive information flow gated model for RST-style text-level discourse parsing. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11839, pp. 231\u2013241. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32236-6_20"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xing, Y., Kong, F., Li, P., Zhou, G.: A top-down neural architecture towards text-level parsing of discourse rhetorical structure, arXiv preprint arXiv:2005.02680 2020","DOI":"10.18653\/v1\/2020.acl-main.569"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, L., Kong, F., Zhou, G.,: Adversarial learning for discourse rhetorical structure parsing. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 3946\u20133957 (2021)","DOI":"10.18653\/v1\/2021.acl-long.305"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Kobayashi, N., Hirao, T., Nakamura, K., Kamigaito, H., Okumura, M., Nagata, M.: Split or merge: Which is better for unsupervised rst parsing? 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. 5797\u20135802 (2019)","DOI":"10.18653\/v1\/D19-1587"},{"key":"17_CR25","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: Generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"3","key":"17_CR26","first-page":"243","volume":"8","author":"WC Mann","year":"1988","unstructured":"Mann, W.C., Thompson, S.A.: Rhetorical structure theory: toward a functional theory of text organization. Text-Interdiscp. J. Study Discourse 8(3), 243\u2013281 (1988)","journal-title":"Text-Interdiscp. J. Study Discourse"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kamigaito, H., Okumura, M.: A language model-based generative classifier for sentence-level discourse parsing. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2432\u20132446 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.188"},{"key":"17_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-030-88480-2_2","volume-title":"Natural Language Processing and Chinese Computing","author":"Y Fan","year":"2021","unstructured":"Fan, Y., Jiang, F., Chu, X., Li, P., Zhu, Q.: Chinese macro discourse parsing on dependency graph convolutional network. In: Wang, L., Feng, Y., Hong, Yu., He, R. (eds.) NLPCC 2021. LNCS (LNAI), vol. 13028, pp. 15\u201326. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88480-2_2"},{"issue":"2","key":"17_CR29","first-page":"321","volume":"31","author":"X Chu","year":"2020","unstructured":"Chu, X., Xi, X., Jiang, F., Xu, S., Zhu, Q., Zhou, G.: Macro discourse structure representation schema and corpus construction. J. Softw. 31(2), 321\u2013343 (2020)","journal-title":"J. Softw."},{"key":"17_CR30","unstructured":"Khosla, P., et al.: In: Supervised contrastive learning, In: Advances in Neural Information Processing Systems, vol. 33, pp. 18 661\u201318 673 (2020)"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2022: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20865-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:49:07Z","timestamp":1667519347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20865-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208645","9783031208652"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20865-2_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shangai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pricai.org\/2022\/","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":"432","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":"91","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":"39","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":"21% - 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":"7-8","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":"n\/a","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)"}}]}}