{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:39:24Z","timestamp":1783438764710,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":81,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Alibaba Research Intern Program"},{"name":"National Science Foundation of China","award":["62006142"],"award-info":[{"award-number":["62006142"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612336","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:26:54Z","timestamp":1698391614000},"page":"6132-6142","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["UniSA: Unified Generative Framework for Sentiment Analysis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6409-8623","authenticated-orcid":false,"given":"Zaijing","family":"Li","sequence":"first","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0650-7286","authenticated-orcid":false,"given":"Ting-En","family":"Lin","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8389-6101","authenticated-orcid":false,"given":"Yuchuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1582-5764","authenticated-orcid":false,"given":"Meng","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Jianzhu University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2414-4802","authenticated-orcid":false,"given":"Fengxiao","family":"Tang","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2317-5359","authenticated-orcid":false,"given":"Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4504-2163","authenticated-orcid":false,"given":"Yongbin","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei.","author":"Bao Hangbo","year":"2022","unstructured":"Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei. 2022. Vlmo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts. Advances in Neural Information Processing Systems (2022), 32897--32912."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S18-1003"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S19-2007"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-008-9076-6"},{"key":"e_1_3_2_1_5_1","volume-title":"Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_6_1","volume-title":"Gate-Variants of Gated Recurrent Unit Neural Networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems. 1597--1600","author":"Dey Rahul","year":"2017","unstructured":"Rahul Dey and Fathi M Salem. 2017. Gate-Variants of Gated Recurrent Unit Neural Networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems. 1597--1600."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the Thirteenth Language Resources and Evaluation Conference. 4096--4113","author":"Feng Shutong","year":"2022","unstructured":"Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Michael Heck, Carel van Niekerk, and Milica Gasic. 2022. EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems. In Proceedings of the Thirteenth Language Resources and Evaluation Conference. 4096--4113."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3592081"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.224"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1015"},{"key":"e_1_3_2_1_11_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1280"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics. 553--569","author":"He Wanwei","year":"2022","unstructured":"Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, and Yongbin Li. 2022a. SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding. In Proceedings of the 29th International Conference on Computational Linguistics. 553--569."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532069"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21320"},{"key":"e_1_3_2_1_16_1","volume-title":"Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.547"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.534"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.440"},{"key":"e_1_3_2_1_20_1","volume-title":"EmoBerta: Speaker-aware Emotion Recognition in Conversation with Roberta. arXiv:2108.12009","author":"Kim Taewoon","year":"2021","unstructured":"Taewoon Kim and Piek Vossen. 2021. EmoBerta: Speaker-aware Emotion Recognition in Conversation with Roberta. arXiv:2108.12009 (2021)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Jan Koco\u0144 Igor Cichecki Oliwier Kaszyca Mateusz Kochanek Dominika Szyd\u0142o Joanna Baran Julita Bielaniewicz Marcin Gruza Arkadiusz Janz Kamil Kanclerz et al. 2023. ChatGPT: Jack of all trades master of none. Information Fusion (2023) 101861.","DOI":"10.1016\/j.inffus.2023.101861"},{"key":"e_1_3_2_1_22_1","unstructured":"Joosung Lee and Wooin Lee. 2022. CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21344"},{"key":"e_1_3_2_1_25_1","volume-title":"GraphCFC: A Directed Graph based Cross-modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition. arXiv:2207.12261","author":"Li Jiang","year":"2022","unstructured":"Jiang Li, Xiaoping Wang, Guoqing Lv, and Zhigang Zeng. 2022c. GraphCFC: A Directed Graph based Cross-modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition. arXiv:2207.12261 (2022)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.631"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21348"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.202"},{"key":"e_1_3_2_1_29_1","volume-title":"Dailydialog: A Manually Labelled Multi-turn Dialogue Dataset. arXiv:1710.03957","author":"Li Yanran","year":"2017","unstructured":"Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. Dailydialog: A Manually Labelled Multi-turn Dialogue Dataset. arXiv:1710.03957 (2017)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.126"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539209"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6353"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/E17-2091"},{"key":"e_1_3_2_1_34_1","volume-title":"Roberta: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_35_1","volume-title":"Scalevlad: Improving Multimodal Sentiment Analysis via Multi-scale Fusion of Locally Descriptors. arXiv:2112.01368","author":"Luo Huaishao","year":"2021","unstructured":"Huaishao Luo, Lei Ji, Yanyong Huang, Bin Wang, Shenggong Ji, and Tianrui Li. 2021. Scalevlad: Improving Multimodal Sentiment Analysis via Multi-scale Fusion of Locally Descriptors. arXiv:2112.01368 (2021)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1344"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/568"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/2002472.2002491"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5347"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016818"},{"key":"e_1_3_2_1_41_1","volume-title":"DialogueTRM: Exploring the Intra-and Inter-modal Emotional Behaviors in the Conversation. arXiv:2010.07637","author":"Mao Yuzhao","year":"2020","unstructured":"Yuzhao Mao, Qi Sun, Guang Liu, Xiaojie Wang, Weiguo Gao, Xuan Li, and Jianping Shen. 2020. DialogueTRM: Exploring the Intra-and Inter-modal Emotional Behaviors in the Conversation. arXiv:2010.07637 (2020)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S18-1001"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1018"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548180"},{"key":"e_1_3_2_1_46_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. ArXiv Vol. abs\/2303.08774 (2023)."},{"key":"e_1_3_2_1_47_1","volume-title":"Orph\u00e9e De Clercq, et al","author":"Pontiki Maria","year":"2016","unstructured":"Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammed AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orph\u00e9e De Clercq, et al. 2016. Semeval-2016 Task 5: Aspect based Sentiment Analysis. In ProWorkshop on Semantic Evaluation 2016. 19--30."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/S14-2004"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1081"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1050"},{"key":"e_1_3_2_1_51_1","volume-title":"Empathetic Response Generation via Emotion Cause Transition Graph. arXiv:2302.11787","author":"Qian Yushan","year":"2023","unstructured":"Yushan Qian, Bo Wang, Ting-En Lin, Yinhe Zheng, Ying Zhu, Dongming Zhao, Yuexian Hou, Yuchuan Wu, and Yongbin Li. 2023. Empathetic Response Generation via Emotion Cause Transition Graph. arXiv:2302.11787 (2023)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462829"},{"key":"e_1_3_2_1_53_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language Models Are Unsupervised Multitask Learners. OpenAI blog (2019) 9."},{"key":"e_1_3_2_1_54_1","volume-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. The Journal of Machine Learning Research","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. The Journal of Machine Learning Research (2020), 5485--5551."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.442"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S17-2088"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016940"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17625"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.123"},{"key":"e_1_3_2_1_60_1","volume-title":"SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue in Multiple Domains. arXiv:2305.13040","author":"Si Shuzheng","year":"2023","unstructured":"Shuzheng Si, Wentao Ma, Yuchuan Wu, Yinpei Dai, Haoyu Gao, Ting-En Lin, Hangyu Li, Rui Yan, Fei Huang, and Yongbin Li. 2023. SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue in Multiple Domains. arXiv:2305.13040 (2023)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1170"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.347"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1656"},{"key":"e_1_3_2_1_64_1","volume-title":"Attention is All You Need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_65_1","volume-title":"Titan: Exploring Larger-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation. arXiv:2112.12731","author":"Wang Shuohuan","year":"2021","unstructured":"Shuohuan Wang, Yu Sun, Yang Xiang, Zhihua Wu, Siyu Ding, Weibao Gong, Shikun Feng, Junyuan Shang, Yanbin Zhao, Chao Pang, et al. 2021. Ernie 3.0 Titan: Exploring Larger-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation. arXiv:2112.12731 (2021)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1058"},{"key":"e_1_3_2_1_67_1","unstructured":"Jason Wei Yi Tay Rishi Bommasani Colin Raffel Barret Zoph Sebastian Borgeaud Dani Yogatama Maarten Bosma Denny Zhou Donald Metzler et al. 2022. Emergent Abilities of Large Language Models. arXiv:2206.07682 (2022)."},{"key":"e_1_3_2_1_68_1","volume-title":"Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems","author":"Xie Qizhe","year":"2020","unstructured":"Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020. Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (2020), 6256--6268."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.44"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.188"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.451"},{"key":"e_1_3_2_1_72_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized Autoregressive Pretraining for Language Understanding. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.438"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17289"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1115"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12021"},{"key":"e_1_3_2_1_77_1","volume-title":"Mosi: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos. arXiv:1606.06259","author":"Zadeh Amir","year":"2016","unstructured":"Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis-Philippe Morency. 2016. Mosi: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos. arXiv:1606.06259 (2016)."},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1208"},{"key":"e_1_3_2_1_79_1","volume-title":"Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks. arXiv:1708.04299","author":"Zahiri Sayyed M","year":"2017","unstructured":"Sayyed M Zahiri and Jinho D Choi. 2017. Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks. arXiv:1708.04299 (2017)."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.27"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21431"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612336","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612336","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T23:58:28Z","timestamp":1755820708000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":81,"alternative-id":["10.1145\/3581783.3612336","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612336","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}