{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:58:49Z","timestamp":1772553529443,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030884796","type":"print"},{"value":"9783030884802","type":"electronic"}],"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\/978-3-030-88480-2_66","type":"book-chapter","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T11:04:52Z","timestamp":1633950292000},"page":"822-833","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Multi-modal Sarcasm Detection Based on Contrastive Attention Mechanism"],"prefix":"10.1007","author":[{"given":"Xiaoqiang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Guangyuan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"66_CR1","doi-asserted-by":"crossref","unstructured":"Castro, S., Hazarika, D., P\u00e9rez-Rosas, V., et al.: Towards multimodal sarcasm detection (an obviously perfect paper). In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4619\u20134629 (2019)","DOI":"10.18653\/v1\/P19-1455"},{"key":"66_CR2","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., et al.: Tensor fusion network for multimodal sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1103\u20131114 (2017)","DOI":"10.18653\/v1\/D17-1115"},{"key":"66_CR3","doi-asserted-by":"crossref","unstructured":"Firdaus, M., Chauhan, H., Ekbal, A., et al.: MEISD: a multimodal multi-label emotion, intensity and sentiment dialogue dataset for emotion recognition and sentiment analysis in conversations. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4441\u20134453 (2020)","DOI":"10.18653\/v1\/2020.coling-main.393"},{"key":"66_CR4","doi-asserted-by":"crossref","unstructured":"Song, C., Huang, Y., Ouyang, W., et al.: Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp, 1179\u20131188 (2018)","DOI":"10.1109\/CVPR.2018.00129"},{"key":"66_CR5","doi-asserted-by":"crossref","unstructured":"Duan, X., Yu, H., Yin, M., et al.: Contrastive attention mechanism for abstractive sentence summarization. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 3044\u20133053 (2019)","DOI":"10.18653\/v1\/D19-1301"},{"key":"66_CR6","unstructured":"Riloff, E., Qadir, A., Surve, P., et al.: Sarcasm as contrast between a positive sentiment and negative situation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 704\u2013714 (2013)"},{"key":"66_CR7","unstructured":"Maynard, D.G., Greenwood, M.A.: Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In: Proceedings of the 9th Conference on Language Resources and Evaluation, pp. 4238\u20134243 (2014)"},{"key":"66_CR8","unstructured":"Van Hee, C.: Can Machines Sense Irony?: Exploring Automatic Irony Detection on Social Media. Ghent University (2017)"},{"key":"66_CR9","unstructured":"Zhang, M., Zhang, Y., Fu, G.: Tweet sarcasm detection using deep neural network. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: technical papers. pp. 2449\u20132460 (2016)"},{"key":"66_CR10","unstructured":"Poria, S., Cambria, E., Hazarika, D., et al.: A deeper look into sarcastic tweets using deep convolutional neural networks. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1601\u20131612 (2016)"},{"issue":"1","key":"66_CR11","first-page":"29","volume":"55","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Du, J., Xu, R.: Sarcasm detection based on adversarial learning. Beijing Da Xue Xue Bao 55(1), 29\u201336 (2019)","journal-title":"Beijing Da Xue Xue Bao"},{"key":"66_CR12","doi-asserted-by":"crossref","unstructured":"Babanejad, N., Davoudi, H., An, A., et al.: Affective and contextual embedding for sarcasm detection. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 225\u2013243 (2020)","DOI":"10.18653\/v1\/2020.coling-main.20"},{"key":"66_CR13","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Liang, P.P., Mazumder, N., et al.: Memory fusion network for multi-view sequential learning.Proc. AAAI Conf. Artif. Intell. 32(1) (2018)","DOI":"10.1609\/aaai.v32i1.12021"},{"key":"66_CR14","unstructured":"Zadeh, A.B., Liang, P.P., Poria, S., et al.: Multimodal language analysis in the wild: CMU-mosei dataset and interpretable dynamic fusion graph. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers). pp. 2236\u20132246 (2018)"},{"issue":"1","key":"66_CR15","first-page":"6892","volume":"33","author":"H Pham","year":"2019","unstructured":"Pham, H., Liang, P.P., Manzini, T., et al.: Found in translation: learning robust joint representations by cyclic translations between modalities. Proc. AAAI Conf. Artif. Intell. 33(1), 6892\u20136899 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"66_CR16","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H.H., Bai, S., Liang, P.P., et al.: Multimodal transformer for unaligned multimodal language sequences. In Proceedings of the Conference, Association for Computational Linguistics, Meeting, NIH Public Access, 2019. p. 6558 (2019)","DOI":"10.18653\/v1\/P19-1656"},{"key":"66_CR17","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al. Attention is all you need. In: NIPS, pp. 5998\u20136008 (2017)"},{"key":"66_CR18","doi-asserted-by":"crossref","unstructured":"Hazarika, D., Poria, S., Mihalcea, R., et al. ICON: interactive conversational memory network for multimodal emotion detection. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2594\u20132604 (2018)","DOI":"10.18653\/v1\/D18-1280"},{"issue":"1","key":"66_CR19","first-page":"6818","volume":"33","author":"N Majumder","year":"2019","unstructured":"Majumder, N., Poria, S., Hazarika, D., et al.: DialogueRNN: an attentive rnn for emotion detection in conversations. Proc. AAAI Conf. Artif. Intell. 33(1), 6818\u20136825 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"66_CR20","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., et al.: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR (2014). https:\/\/arxiv.org\/abs\/1412.3555"},{"key":"66_CR21","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Majumder, N., Poria, S., et al.: 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, pp. 154\u2013164 (2019)","DOI":"10.18653\/v1\/D19-1015"},{"key":"66_CR22","doi-asserted-by":"crossref","unstructured":"Ishiwatari, T., Yasuda, Y., Miyazaki, T., et al.: 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, pp. 7360\u20137370 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.597"},{"key":"66_CR23","doi-asserted-by":"crossref","unstructured":"Delbrouck, J.B., Tits, N., Dupont, S.: Modulated fusion using transformer for linguistic-acoustic emotion recognition. In: Proceedings of the First International Workshop on Natural Language Processing Beyond Text, 20 November 2020, pp. 1\u201310 (2020)","DOI":"10.18653\/v1\/2020.nlpbt-1.1"},{"key":"66_CR24","unstructured":"Devlin, J., Chang, M.W., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding (2018)"},{"key":"66_CR25","unstructured":"McFee, B., McVicar, M., Balke, S., et al.: librosa\/librosa: 0.6.2 (2018)"},{"key":"66_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"66_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, W., Li, S., et al.: Modeling both intra-and inter-modal influence for real-time emotion detection in conversations. In: Proceedings of ACM Multimedia, pp. 503\u2013511 (2020)","DOI":"10.1145\/3394171.3413949"},{"key":"66_CR28","unstructured":"Kingma, D.P., Ba, J.: ADAM: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR)"},{"key":"66_CR29","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. In: Journal of Machine Learning Research, pp. 1929\u20131958 (2014)"}],"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\/978-3-030-88480-2_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:51:32Z","timestamp":1709826692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88480-2_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030884796","9783030884802"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88480-2_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}