{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:56:36Z","timestamp":1726235796441},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466601"},{"type":"electronic","value":"9783031466618"}],"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\/978-3-031-46661-8_21","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"309-323","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploiting Pseudo Future Contexts for\u00a0Emotion Recognition in\u00a0Conversations"],"prefix":"10.1007","author":[{"given":"Yinyi","family":"Wei","sequence":"first","affiliation":[]},{"given":"Shuaipeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hailei","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Mo","sequence":"additional","affiliation":[]},{"given":"Guanglu","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., Choi, Y.: Comet: commonsense transformers for automatic knowledge graph construction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4762\u20134779 (2019)","DOI":"10.18653\/v1\/P19-1470"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Busso, C., et al.: Iemocap: interactive emotional dyadic motion capture database. Lang. Resour. Eval. 42(4), 335\u2013359 (2008)","DOI":"10.1007\/s10579-008-9076-6"},{"issue":"2","key":"21_CR3","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Majumder, N., Gelbukh, A., Mihalcea, R., Poria, S.: Cosmic: commonsense knowledge for emotion identification in conversations. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2470\u20132481 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.224"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Majumder, N., Mihalcea, R., Poria, S.: Exploring the role of context in utterance-level emotion, act and intent classification in conversations: an empirical study. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1435\u20131449 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.124"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Majumder, N., Poria, S., Chhaya, N., Gelbukh, A.: Dialoguegcn: a graph convolutional neural network for emotion recognition in conversation. 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. 154\u2013164 (2019)","DOI":"10.18653\/v1\/D19-1015"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Hu, D., Wei, L., Huai, X.: Dialoguecrn: contextual reasoning networks for emotion recognition in conversations. 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. 7042\u20137052 (2021)","DOI":"10.18653\/v1\/2021.acl-long.547"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Ishiwatari, T., Yasuda, Y., Miyazaki, T., Goto, J.: Relation-aware graph attention networks with relational position encodings for emotion recognition in conversations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7360\u20137370 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.597"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Li, J., Lin, Z., Fu, P., Wang, W.: Past, present, and future: conversational emotion recognition through structural modeling of psychological knowledge. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 1204\u20131214 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.104"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Li, J., Ji, D., Li, F., Zhang, M., Liu, Y.: Hitrans: a transformer-based context-and speaker-sensitive model for emotion detection in conversations. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4190\u20134200 (2020)","DOI":"10.18653\/v1\/2020.coling-main.370"},{"key":"21_CR11","doi-asserted-by":"crossref","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, vol. 36, pp. 11002\u201311010 (2022)","DOI":"10.1609\/aaai.v36i10.21348"},{"key":"21_CR12","unstructured":"Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S.: Dailydialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 986\u2013995 (2017)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Lin, Z., Madotto, A., Shin, J., Xu, P., Fung, P.: Moel: mixture of empathetic listeners. 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. 121\u2013132 (2019)","DOI":"10.18653\/v1\/D19-1012"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Ling, T., Chen, L., Lai, Y., Liu, H.L.: Evolutionary verbalizer search for prompt-based few shot text classification. arXiv preprint arXiv:2306.10514 (2023)","DOI":"10.1007\/978-3-031-40292-0_23"},{"key":"21_CR15","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., Cambria, E.: Dialoguernn: an attentive RNN for emotion detection in conversations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6818\u20136825 (2019)","DOI":"10.1609\/aaai.v33i01.33016818"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R.: Meld: a multimodal multi-party dataset for emotion recognition in conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 527\u2013536 (2019)","DOI":"10.18653\/v1\/P19-1050"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. 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. 464\u2013468 (2018)","DOI":"10.18653\/v1\/N18-2074"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Shen, W., Wu, S., Yang, Y., Quan, X.: Directed acyclic graph network for conversational emotion recognition. 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. 1551\u20131560 (2021)","DOI":"10.18653\/v1\/2021.acl-long.123"},{"key":"21_CR20","doi-asserted-by":"publisher","unstructured":"Shu, K., Mosallanezhad, A., Liu, H.: Cross-domain fake news detection on social media: a context-aware adversarial approach. In: Khosravy, M., Echizen, I., Babaguchi, N. (eds.) Frontiers in Fake Media Generation and Detection, pp. 215\u2013232. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-19-1524-6_9","DOI":"10.1007\/978-981-19-1524-6_9"},{"key":"21_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Ma, J., Wang, S., Xiao, J.: Contextualized emotion recognition in conversation as sequence tagging. In: Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 186\u2013195 (2020)","DOI":"10.18653\/v1\/2020.sigdial-1.23"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Wei, Y., Mo, T., Jiang, Y., Li, W., Zhao, W.: Eliciting knowledge from pretrained language models for prototypical prompt verbalizer. In: Artificial Neural Networks and Machine Learning-ICANN 2022: 31st International Conference on Artificial Neural Networks, pp. 222\u2013233 (2022)","DOI":"10.1007\/978-3-031-15931-2_19"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Yang, L., Shen, Y., Mao, Y., Cai, L.: Hybrid curriculum learning for emotion recognition in conversation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 11595\u201311603 (2022)","DOI":"10.1609\/aaai.v36i10.21413"},{"key":"21_CR25","unstructured":"Zahiri, S.M., Choi, J.D.: Emotion detection on tv show transcripts with sequence-based convolutional neural networks. In: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence (2018)"},{"key":"21_CR26","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":"21_CR27","doi-asserted-by":"crossref","unstructured":"Zhong, P., Wang, D., Miao, C.: Knowledge-enriched transformer for emotion detection in textual conversations. 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. 165\u2013176 (2019)","DOI":"10.18653\/v1\/D19-1016"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Zhu, L., Pergola, G., Gui, L., Zhou, D., He, Y.: Topic-driven and knowledge-aware transformer for dialogue emotion detection. 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. 1571\u20131582 (2021)","DOI":"10.18653\/v1\/2021.acl-long.125"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46661-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T08:20:17Z","timestamp":1703492417000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46661-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466601","9783031466618"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46661-8_21","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":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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)"}}]}}