{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:15:59Z","timestamp":1750220159298,"version":"3.41.0"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"crossref","award":["J2019-IV-0002-0069"],"award-info":[{"award-number":["J2019-IV-0002-0069"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61572120"],"award-info":[{"award-number":["61572120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["N181602013"],"award-info":[{"award-number":["N181602013"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>\n            Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this article, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over-sensitivity, over-stability, and generalization. Specifically, we first use a natural language inference module to help the model understand the accurate semantic meanings of input questions to address the issues of over-sensitivity and over-stability. Then, in the machine reading comprehension module, we propose a memory-guided multi-head attention method that can further well understand the semantic meanings of input questions and passages. Third, we propose a multi-language learning mechanism to address the issue of generalization. Finally, these modules are integrated with a multi-task learning-based method. We evaluate our model on three benchmark datasets that are designed to measure models\u2019 robustness, including DuReader (robust) and two SQuAD-related datasets. Extensive experiments show that our model can well address the mentioned three kinds of robustness issues. And it achieves much better results than the compared state-of-the-art models on all these datasets under different evaluation metrics, even under some extreme and unfair evaluations. The source code of our work is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/neukg\/RobustMRC\">https:\/\/github.com\/neukg\/RobustMRC<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3546190","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T10:18:50Z","timestamp":1656584330000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["An Understanding-oriented Robust Machine Reading Comprehension Model"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6824-1191","authenticated-orcid":false,"given":"Feiliang","family":"Ren","sequence":"first","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-0225","authenticated-orcid":false,"given":"Yongkang","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2897-3886","authenticated-orcid":false,"given":"Bochao","family":"Li","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2976-6256","authenticated-orcid":false,"given":"Shilei","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3528-773X","authenticated-orcid":false,"given":"Bingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9306-8757","authenticated-orcid":false,"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0028-8425","authenticated-orcid":false,"given":"Chunchao","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9548-1350","authenticated-orcid":false,"given":"Qi","family":"Ma","sequence":"additional","affiliation":[{"name":"Northeastern University, Heping Qu, Shenyang Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.95"},{"key":"e_1_3_2_3_1","doi-asserted-by":"crossref","unstructured":"Razieh Baradaran and Hossein Amirkhani. 2021. Ensemble learning-based approach for improving generalization capability of machine reading comprehension systems. Neurocomputing 466 (2021) 229\u2013242.","DOI":"10.1016\/j.neucom.2021.08.095"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.696"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1084"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1075"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.324"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.98"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.567"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.starsem-1.7"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.241"},{"key":"e_1_3_2_12_1","article-title":"SmarNet: Teaching machines to read and comprehend like human.","author":"Chen Zheqian","year":"2017","unstructured":"Zheqian Chen, Rongqin Yang, Bin Cao, Zhou Zhao, Deng Cai, and Xiaofei He. 2017. SmarNet: Teaching machines to read and comprehend like human. arXiv preprint arXiv:1710.02772 (2017).","journal-title":"arXiv preprint arXiv:1710.02772"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1078"},{"key":"e_1_3_2_14_1","article-title":"Pre-training with whole word masking for Chinese BERT","author":"Cui Yiming","year":"2019","unstructured":"Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, and Guoping Hu. 2019. Pre-training with whole word masking for Chinese BERT. arXiv preprint arXiv:1906.08101 (2019).","journal-title":"arXiv preprint arXiv:1906.08101"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1055"},{"key":"e_1_3_2_16_1","first-page":"4171","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N. Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171\u20134186."},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1213"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1610"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.603"},{"key":"e_1_3_2_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.237"},{"key":"e_1_3_2_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.83"},{"key":"e_1_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-2605"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1607"},{"key":"e_1_3_2_24_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1221"},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1232"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016529"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.420"},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.242"},{"key":"e_1_3_2_29_1","first-page":"177","volume-title":"Proceedings of the 1st International Conference on Machine Learning Challenges: Evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment","author":"Glickman Bernardo Magnini, Ido Dagan, and Oren","year":"2006","unstructured":"Bernardo Magnini, Ido Dagan, and Oren Glickman. 2006. The PASCAL recognising textual entailment challenge. In Proceedings of the 1st International Conference on Machine Learning Challenges: Evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment. 177\u2013190."},{"key":"e_1_3_2_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1215"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.417"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1147"},{"key":"e_1_3_2_33_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Sabharwal Peter Clark, Tushar Khot, and Ashish","year":"2018","unstructured":"Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. SciTaiL: A textual entailment dataset from science question answering. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_34_1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations."},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.164"},{"key":"e_1_3_2_36_1","volume-title":"Proceedings of the 8th International Conference on Learning Representations","author":"Lan Zhenzhong","year":"2020","unstructured":"Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A lite BERT for self-supervised learning of language representations. In Proceedings of the 8th International Conference on Learning Representations."},{"key":"e_1_3_2_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.111"},{"key":"e_1_3_2_38_1","first-page":"146","volume-title":"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop","author":"Li Hongyu","year":"2020","unstructured":"Hongyu Li, Tengyang Chen, Shuting Bai, Takehito Utsuro, and Yasuhide Kawada. 2020a. MRC examples answerable by BERT without a question are less effective in MRC model training. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop. 146\u2013152."},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.65"},{"key":"e_1_3_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6357"},{"key":"e_1_3_2_41_1","first-page":"1952","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"Liu Xin","year":"2018","unstructured":"Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, and Buzhou Tang. 2018a. LCQMC: A large-scale Chinese question matching corpus. In Proceedings of the 27th International Conference on Computational Linguistics. 1952\u20131962."},{"key":"e_1_3_2_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1157"},{"key":"e_1_3_2_43_1","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","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 preprint arXiv:1907.11692 (2019).","journal-title":"arXiv preprint arXiv:1907.11692"},{"key":"e_1_3_2_44_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.329"},{"key":"e_1_3_2_45_1","first-page":"687","volume-title":"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing","author":"Luo Huaishao","year":"2020","unstructured":"Huaishao Luo, Yu Shi, Ming Gong, Linjun Shou, and Tianrui Li. 2020. MaP: A matrix-based prediction approach to improve span extraction in machine reading comprehension. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. 687\u2013695."},{"key":"e_1_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.conll-1.11"},{"key":"e_1_3_2_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-short.109"},{"key":"e_1_3_2_48_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1160"},{"key":"e_1_3_2_49_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.wnut-1.38"},{"key":"e_1_3_2_50_1","volume-title":"Proceedings of the CoCo@NIPS Workshop","author":"Nguyen Tri","year":"2016","unstructured":"Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In Proceedings of the CoCo@NIPS Workshop."},{"key":"e_1_3_2_51_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Shou Min Gong, Jian Pei, Daxin Jiang, Nuo Chen, and Linjun","year":"2022","unstructured":"Min Gong, Jian Pei, Daxin Jiang, Nuo Chen, and Linjun Shou. 2022. From good to best: Two-stage training for cross-lingual machine reading comprehension. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_52_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.235"},{"key":"e_1_3_2_53_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2124"},{"key":"e_1_3_2_54_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-short.10"},{"key":"e_1_3_2_55_1","volume-title":"Proceedings of the International Conference on Learning Representations (Poster)","author":"Seo Min Joon","year":"2016","unstructured":"Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. In Proceedings of the International Conference on Learning Representations (Poster)."},{"key":"e_1_3_2_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-srw.21"},{"key":"e_1_3_2_57_1","article-title":"Benchmarking robustness of machine reading comprehension models.","author":"Si Chenglei","year":"2020","unstructured":"Chenglei Si, Ziqing Yang, Yiming Cui, Wentao Ma, Ting Liu, and Shijin Wang. 2020. Benchmarking robustness of machine reading comprehension models. arXiv preprint arXiv:2004.14004 (2020).","journal-title":"arXiv preprint arXiv:2004.14004"},{"key":"e_1_3_2_58_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1270"},{"key":"e_1_3_2_59_1","article-title":"ERNIE: Enhanced representation through knowledge integration","author":"Sun Yu","year":"2019","unstructured":"Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019a. ERNIE: Enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223 (2019).","journal-title":"arXiv preprint arXiv:1904.09223"},{"key":"e_1_3_2_60_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-short.120"},{"key":"e_1_3_2_61_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.247"},{"key":"e_1_3_2_62_1","first-page":"5998","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_2_63_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1219"},{"key":"e_1_3_2_64_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1158"},{"key":"e_1_3_2_65_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2091"},{"key":"e_1_3_2_66_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1178"},{"key":"e_1_3_2_67_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1159"},{"key":"e_1_3_2_68_1","first-page":"Association for","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2020","author":"Welbl Johannes","year":"2020","unstructured":"Johannes Welbl, Pasquale Minervini, Max Bartolo, Pontus Stenetorp, and Sebastian Riedel. 2020. Undersensitivity in neural reading comprehension. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, 1152\u20131165."},{"key":"e_1_3_2_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1363"},{"key":"e_1_3_2_70_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1101"},{"key":"e_1_3_2_71_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106075"},{"key":"e_1_3_2_72_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017354"},{"key":"e_1_3_2_73_1","first-page":"5753","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019. XLNet: Generalized autoregressive pretraining for language understanding. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 5753\u20135763."},{"key":"e_1_3_2_74_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Yu Adams Wei","year":"2018","unstructured":"Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018. QANet: Combining local convolution with global self-attention for reading comprehension. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_75_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.236"},{"key":"e_1_3_2_76_1","volume-title":"AAAI 2021","author":"Zhang Zhuosheng","year":"2021","unstructured":"Zhuosheng Zhang, Junjie Yang, and Hai Zhao. 2021. Retrospective reader for machine reading comprehension. In AAAI 2021."},{"key":"e_1_3_2_77_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.599"},{"key":"e_1_3_2_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2020.3016132"},{"key":"e_1_3_2_79_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.773"},{"key":"e_1_3_2_80_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.internlp-1.1"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3546190","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3546190","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:23Z","timestamp":1750186823000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3546190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":79,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3,31]]}},"alternative-id":["10.1145\/3546190"],"URL":"https:\/\/doi.org\/10.1145\/3546190","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2022,12,27]]},"assertion":[{"value":"2021-08-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}