{"type":"ScholarlyArticle","title":"Modeling Selective Feature Attention for Lightweight Text Matching","authors":[{"name":"Jianxiang Zang","slug":"jianxiang-zang","given":"Jianxiang","family":"Zang","affiliation":"School of Statistics and Information , Shanghai University of International Business and Economics"},{"name":"Hui Liu","slug":"hui-liu","given":"Hui","family":"Liu","affiliation":"School of Statistics and Information , Shanghai University of International Business and Economics"}],"abstract":"Representation-based Siamese networks have risen to popularity in lightweight text matching due to their low deployment and inference costs. While word-level attention mechanisms have been implemented within Siamese networks to improve performance, we propose Feature Attention (FA), a novel downstream block designed to enrich the modeling of dependencies among embedding features. Employing \"squeeze-and-excitation\" techniques, the FA block dynamically adjusts the emphasis on individual features, enabling the network to concentrate more on features that significantly contribute to the final classification. Building upon FA, we introduce a dynamic \"selection\" mechanism called Selective Feature Attention (SFA), which leverages a stacked BiGRU Inception structure. The SFA block facilitates multi-scale semantic extraction by traversing different stacked BiGRU layers, encouraging the network to selectively concentrate on semantic information and embedding features across varying levels of abstraction. Both the FA and SFA blocks offer a seamless integration capability with various Siamese networks, showcasing a plug-and-play characteristic. Experimental evaluations conducted across diverse text matching baselines and benchmarks underscore the indispensability of modeling feature attention and the superiority of the \"selection\" mechanism.","doi":"10.24963/ijcai.2024/732","url":"https://ijcai.org/proceedings/2024/732","pdf":"https://ijcai.org/proceedings/2024/0732.pdf","markdown":"https://ijcai.org/proceedings/2024/732.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/732","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":732,"pages":{"first":6624,"last":6632},"keywords":["Machine Learning: ML: Deep learning architectures","Machine Learning: ML: Attention models","Natural Language Processing: NLP: Embeddings","Natural Language Processing: NLP: Natural language semantics"],"videos":[],"references":[{"position":1,"raw":"[Bello et al., 2019] Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le. Attention augmented convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3286-3295, 2019.","title":"Attention augmented convolutional networks","authors":"Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, Quoc V Le","year":"2019","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bowman et al., 2015] Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632-642, 2015.","title":"A large annotated corpus for learning natural language inference","authors":"Bowman","year":"2015","venue":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2020] Qingqing Cao, Harsh Trivedi, Aruna Bal- asubramanian, and Niranjan Balasubramanian. Deformer: Decomposing pre-trained transformers for faster question answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4487-4497, 2020.","title":"Deformer: Decomposing pre-trained transformers for faster question answering","authors":"Qingqing Cao, Harsh Trivedi, Aruna Balasubramanian, Niranjan Balasubramanian","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2017] Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. Enhanced lstm for natural language inference. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1657-1668, 2017.","title":"Enhanced lstm for natural language inference","authors":"Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen","year":"2017","venue":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Clark et al., 2019] Christopher Clark, Kenton Lee, Ming- Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising dif- ficulty of natural yes/no questions. In Proceedings of NAACL-HLT, pages 2924-2936, 2019.","title":"Boolq: Exploring the surprising difficulty of natural yes/no questions","authors":"Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, Kristina Toutanova","year":"2019","venue":"Proceedings of NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Conneau et al., 2017] A Conneau, D Kiela, H Schwenk, L Barrault, and A Bordes. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 670-680. Association for Computational Linguistics, 2017.","title":"Supervised learning of universal sentence representations from natural language inference data","authors":"Conneau, H Kiela, L Schwenk, Barrault, Bordes","year":"2017","venue":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dolan and Brockett, 2005] Bill Dolan and Chris Brockett. Automatically constructing a corpus of sentential para- phrases. In Third International Workshop on Paraphrasing (IWP2005). Asia Federation of Natural Language Process- ing, January 2005.","title":"Automatically constructing a corpus of sentential paraphrases","authors":"Brockett Dolan, Bill Dolan, Chris Brockett","year":"2005","venue":"Third International Workshop on Paraphrasing (IWP2005)","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fu et al., 2019] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual at- tention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146-3154, 2019.","title":"Dual attention network for scene segmentation","authors":"Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, Hanqing Lu","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gong et al., 2018] Yichen Gong, Heng Luo, and Jian Zhang. Natural language inference over interaction space. In International Conference on Learning Representations, 2018.","title":"Natural language inference over interaction space","authors":"Yichen Gong, Heng Luo, Jian Zhang","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hou et al., 2020] Qibin Hou, Li Zhang, Ming-Ming Cheng, and Jiashi Feng. Strip pooling: Rethinking spatial pooling for scene parsing. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 4003-4012, 2020.","title":"Strip pooling: Rethinking spatial pooling for scene parsing","authors":"Hou","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hu et al., 2018a] Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Andrea Vedaldi. Gather-excite: Exploiting fea- ture context in convolutional neural networks. Advances in neural information processing systems, 31, 2018.","title":"Gather-excite: Exploiting feature context in convolutional neural networks","authors":"Hu","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hu et al., 2018b] Jie Hu, Li Shen, and Gang Sun. Squeeze- and-excitation networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 7132-7141, 2018.","title":"Squeezeand-excitation networks","authors":"Hu","year":"2018","venue":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2013] Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. Learning deep structured semantic models for web search using click- through data. In Proceedings of the 22nd ACM Interna- tional Conference on Information & Knowledge Manage- ment, pages 2333-2338, 2013.","title":"Learning deep structured semantic models for web search using clickthrough data","authors":"Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry Heck","year":"2013","venue":"Proceedings of the 22nd ACM International Conference on Information & Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Huang et al., 2019] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Cc- net: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 603-612, 2019.","title":"Ccnet: Criss-cross attention for semantic segmentation","authors":"Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, Wenyu Liu","year":"2019","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hubel and Wiesel, 1962] David H Hubel and Torsten N Wiesel. Receptive fields, binocular interaction and func- tional architecture in the cat's visual cortex. The Journal of physiology, 160(1):106, 1962.","title":"Receptive fields, binocular interaction and functional architecture in the cat's visual cortex","authors":"H David, Torsten N Hubel, Wiesel","year":"1962","venue":"The Journal of physiology","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Iyer et al., 2017] Shankar Iyer, Nikhil Dandekar, Kornél Csernai, et al. First quora dataset release: Question pairs. data. quora. com. 2017.","title":"First quora dataset release: Question pairs","authors":"Shankar Iyer, Nikhil Dandekar, Kornél Csernai","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Khattab and Zaharia, 2020] Omar Khattab and Matei Za- haria. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 39-48, 2020.","title":"Colbert: Efficient and effective passage search via contextualized late interaction over bert","authors":"Omar Khattab, Matei Zaharia","year":"2020","venue":"Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Khot et al., 2018] Tushar Khot, Ashish Sabharwal, and Pe- ter Clark. Scitail: a textual entailment dataset from science question answering. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Confer- ence and Eighth AAAI Symposium on Educational Ad- vances in Artificial Intelligence, pages 5189-5197, 2018.","title":"Scitail: a textual entailment dataset from science question answering","authors":"Tushar Khot, Ashish Sabharwal, Peter Clark","year":"2018","venue":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kim et al., 2019] Seonhoon Kim, Inho Kang, and No- jun Kwak. Semantic sentence matching with densely- connected recurrent and co-attentive information. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6586-6593, 2019.","title":"Semantic sentence matching with denselyconnected recurrent and co-attentive information","authors":"Kim","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lai et al., 2016] Siwei Lai, Kang Liu, Shizhu He, and Jun Zhao. How to generate a good word embedding. IEEE Intelligent Systems, 31(6):5-14, 2016.","title":"How to generate a good word embedding","authors":"Siwei Lai, Kang Liu, Shizhu He, Jun Zhao","year":"2016","venue":"IEEE Intelligent Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lan et al., 2019] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Sori- cut. Albert: Alite bert for self-supervised learning of lan- guage representations. arXiv preprint arXiv:1909.11942, 2019.","title":"Albert: Alite bert for self-supervised learning of language representations","authors":"Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Li et al., 2019] Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang. Selective kernel networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 510-519, 2019.","title":"Selective kernel networks","authors":"Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Linsley et al., 2019] Drew Linsley, Dan Shiebler, Sven Eberhardt, and Thomas Serre. Learning what and where to attend. In International Conference on Learning Repre- sentations, 2019.","title":"Learning what and where to attend","authors":"Linsley","year":"2019","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liu et al., 2019] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.","title":"Roberta: A robustly optimized bert pretraining approach","authors":"Liu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2021] Hanxiao Liu, Zihang Dai, David So, and Quoc V Le. Pay attention to mlps. Advances in Neural Information Processing Systems, 34:9204-9215, 2021.","title":"Pay attention to mlps","authors":"Liu","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Misra et al., 2021] Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, and Qibin Hou. Rotate to attend: Convolutional triplet attention module. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 3139-3148, 2021.","title":"Rotate to attend: Convolutional triplet attention module","authors":"Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, Qibin Hou","year":"2021","venue":"Proceedings of the IEEE/CVF winter conference on applications of computer vision","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Nelson and Frost, 1978] JI Nelson and BJ Frost. Orientation-selective inhibition from beyond the classic visual receptive field. Brain research, 139(2):359-365, 1978.","title":"Orientation-selective inhibition from beyond the classic visual receptive field","authors":"Nelson, Frost ; Ji Nelson, B J Frost","year":"1978","venue":"Brain research","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Nie and Bansal, 2017] Yixin Nie and Mohit Bansal. Shortcut-stacked sentence encoders for multi-domain inference. RepEval 2017, page 41, 2017.","title":"Shortcut-stacked sentence encoders for multi-domain inference","authors":"Bansal Nie, Yixin Nie, Mohit Bansal","year":"2017","venue":"RepEval","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Sceniak et al., 1999] Michael P Sceniak, Dario L Ringach, Michael J Hawken, and Robert Shapley. Contrast's effect on spatial summation by macaque v1 neurons. Nature neu- roscience, 2(8):733-739, 1999.","title":"Contrast's effect on spatial summation by macaque v1 neurons","authors":"Dario L Michael P Sceniak, Michael J Ringach, Robert Hawken, Shapley","year":"1999","venue":"Nature neuroscience","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Szegedy et al., 2017] Christian Szegedy, Sergey Ioffe, Vin- cent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artifi- cial intelligence, volume 31, 2017.","title":"Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning","authors":"Szegedy","year":"2017","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tay et al., 2018] Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. Compare, compress and propagate: Enhancing neural architectures with alignment factorization for natural lan- guage inference. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1565-1575, 2018.","title":"Compare, compress and propagate: Enhancing neural architectures with alignment factorization for natural language inference","authors":"Yi Tay, Anh Tuan Luu, Siu Cheung, Hui","year":"2018","venue":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Sys- tems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2017] Zhiguo Wang, Wael Hamza, and Radu Florian. Bilateral multi-perspective matching for natural language sentences. In Proceedings of the 26th Interna- tional Joint Conference on Artificial Intelligence, pages 4144-4150, 2017.","title":"Bilateral multi-perspective matching for natural language sentences","authors":"Wang","year":"2017","venue":"Proceedings of the 26th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/579"},{"position":35,"raw":"[Wang et al., 2018] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics, 2018.","title":"Glue: A multi-task benchmark and analysis platform for natural language understanding","authors":"Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel Bowman","year":"2018","venue":"Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Williams et al., 2018] Adina Williams, Nikita Nangia, and Samuel R Bowman. A broad-coverage challenge corpus for sentence understanding through inference. In 2018 Conference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Language Technologies, NAACL HLT 2018, pages 1112-1122. Asso- ciation for Computational Linguistics (ACL), 2018.","title":"A broad-coverage challenge corpus for sentence understanding through inference","authors":"Adina Williams, Nikita Nangia, Samuel R Bowman","year":"2018","venue":"Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Woo et al., 2018] Sanghyun Woo, Jongchan Park, Joon- Young Lee, and In So Kweon. Cbam: Convolutional block attention module. In Proceedings of the European Confer- ence on Computer Vision (ECCV), pages 3-19, 2018.","title":"Cbam: Convolutional block attention module","authors":"Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon","year":"2018","venue":"Proceedings of the European Conference on Computer Vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2019] Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, and Haiqing Chen. Simple and effective text matching with richer alignment features. In Proceedings of the 57th Annual Meeting of the Association for Compu- tational Linguistics, pages 4699-4709, 2019.","title":"Simple and effective text matching with richer alignment features","authors":"Yang","year":"2019","venue":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zang and Liu, 2023a] Jianxiang Zang and Hui Liu. How to extract and interact? nested siamese text matching with interaction and extraction. In International Conference on Artificial Neural Networks, pages 523-535. Springer, 2023.","title":"How to extract and interact? nested siamese text matching with interaction and extraction","authors":"Liu Zang, Jianxiang Zang, Hui Liu","year":"2023","venue":"International Conference on Artificial Neural Networks","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zang and Liu, 2023b] Jianxiang Zang and Hui Liu. Improv- ing text semantic similarity modeling through a 3d siamese network. In ECAI 2023, pages 2970-2977. IOS Press, 2023.","title":"Improving text semantic similarity modeling through a 3d siamese network","authors":"Liu Zang, Jianxiang Zang, Hui Liu","year":"2023","venue":"ECAI 2023","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}