{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T11:20:37Z","timestamp":1773141637080,"version":"3.50.1"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"funder":[{"name":"ONR","award":["N00014-22-1-2206, N00014-18-1-2831"],"award-info":[{"award-number":["N00014-22-1-2206, N00014-18-1-2831"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Hum.-Robot Interact."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Natural language instructions are effective at tasking autonomous robots and for teaching them new knowledge quickly. Yet, human instructors are not perfect and are likely to make mistakes at times and will correct themselves when they notice errors in their own instructions. In this article, we introduce a complete system for robot behaviors to handle such corrections, during both task instruction and action execution. We then demonstrate its operation in an integrated cognitive robotic architecture through spoken language in two tasks: a navigation and retrieval task and a meal assembly task. Verbal corrections occur before, during, and after verbally taught sequences of tasks, demonstrating that the proposed methods enable fast corrections not only of the semantics generated from the instructions but also of overt robot behavior in a manner shown to be reasonable when compared to human behavior and expectations.<\/jats:p>","DOI":"10.1145\/3623385","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T12:06:10Z","timestamp":1695384370000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["\u201cDo This Instead\u201d\u2014Robots That Adequately Respond to Corrected Instructions"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5650-2574","authenticated-orcid":false,"given":"Christopher","family":"Thierauf","sequence":"first","affiliation":[{"name":"Human-Robot Interaction Lab, Tufts University, Medford, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7903-5470","authenticated-orcid":false,"given":"Ravenna","family":"Thielstrom","sequence":"additional","affiliation":[{"name":"Human-Robot Interaction Lab, Tufts University, Medford, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1053-8248","authenticated-orcid":false,"given":"Bradley","family":"Oosterveld","sequence":"additional","affiliation":[{"name":"Thinking Robots, Inc, Boston, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0997-7040","authenticated-orcid":false,"given":"Will","family":"Becker","sequence":"additional","affiliation":[{"name":"Thinking Robots, Inc, Boston, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0064-2789","authenticated-orcid":false,"given":"Matthias","family":"Scheutz","sequence":"additional","affiliation":[{"name":"Human-Robot Interaction Lab, Tufts University, Medford, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1297","volume-title":"Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201919)","author":"Appelgren Mattias","year":"2019","unstructured":"Mattias Appelgren and Alex Lascarides. 2019. Learning plans by acquiring grounded linguistic meanings from corrections. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201919). 1297\u20131305."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-020-09481-8"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364917706418"},{"key":"e_1_3_2_5_2","volume-title":"Proceedings of the RSS Workshop on Model Learning for Human-Robot Communication","author":"Broad Alexander","year":"2016","unstructured":"Alexander Broad, Jacob Arkin, Nathan Ratliff, Thomas Howard, Brenna Argall, and Distributed Correspondence Graph. 2016. Towards real-time natural language corrections for assistive robots. In Proceedings of the RSS Workshop on Model Learning for Human-Robot Communication."},{"key":"e_1_3_2_6_2","article-title":"LaTTe: Language trajectory TransformEr","author":"Bucker Arthur","year":"2022","unstructured":"Arthur Bucker, Luis Figueredo, Sami Haddadin, Ashish Kapoor, Shuang Ma, and Rogerio Bonatti. 2022. LaTTe: Language trajectory TransformEr. Retrieved from https:\/\/arXiv:2208.02918","journal-title":"R"},{"key":"e_1_3_2_7_2","first-page":"978","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201922)","author":"Bucker Arthur","year":"2022","unstructured":"Arthur Bucker, Luis Figueredo, Sami Haddadinl, Ashish Kapoor, Shuang Ma, and Rogerio Bonatti. 2022. Reshaping robot trajectories using natural language commands: A study of multi-modal data alignment using transformers. In Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS\u201922). IEEE, 978\u2013984."},{"key":"e_1_3_2_8_2","first-page":"275","volume-title":"Proceedings of the Human-Robot Interaction Conference","author":"Cantrell Rehj","year":"2010","unstructured":"Rehj Cantrell, Matthias Scheutz, Paul Schermerhorn, and Xuan Wu. 2010. Robust spoken instruction understanding for HRI. In Proceedings of the Human-Robot Interaction Conference. 275\u2013282."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2157689.2157840"},{"key":"e_1_3_2_10_2","article-title":"Overcoming referential ambiguity in language-guided goal-conditioned reinforcement learning","author":"Caselles-Dupr\u00e9 Hugo","year":"2022","unstructured":"Hugo Caselles-Dupr\u00e9, Olivier Sigaud, and Mohamed Chetouani. 2022. Overcoming referential ambiguity in language-guided goal-conditioned reinforcement learning. Retrieved from https:\/\/arXiv:2209.12758","journal-title":"R"},{"key":"e_1_3_2_11_2","first-page":"3949","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems","author":"Choi Dongkyu","year":"2009","unstructured":"Dongkyu Choi, Yeonsik Kang, Heonyoung Lim, and Bum-Jae You. 2009. Knowledge-based control of a humanoid robot. In Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems. IEEE, 3949\u20133954."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2017.05.005"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-009-9094-9"},{"key":"e_1_3_2_14_2","first-page":"93","volume-title":"Proceedings of the ACM\/IEEE International Conference on Human-Robot Interaction","author":"Cui Yuchen","year":"2023","unstructured":"Yuchen Cui, Siddharth Karamcheti, Raj Palleti, Nidhya Shivakumar, Percy Liang, and Dorsa Sadigh. 2023. No, to the right: Online language corrections for robotic manipulation via shared autonomy. In Proceedings of the ACM\/IEEE International Conference on Human-Robot Interaction. 93\u2013101."},{"key":"e_1_3_2_15_2","first-page":"6351","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Dong Qianqian","year":"2019","unstructured":"Qianqian Dong, Feng Wang, Zhen Yang, Wei Chen, Shuang Xu, and Bo Xu. 2019. Adapting translation models for transcript disfluency detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 6351\u20136358."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.5555\/1703775.1704055"},{"key":"e_1_3_2_17_2","volume-title":"Proceedings of the Language Resources and Evaluation Conference (LREC\u201910)","author":"Eberhard Kathleen","year":"2010","unstructured":"Kathleen Eberhard, Hannele Nicholson, Sandra Kuebler, Susan Gundersen, and Matthias Scheutz. 2010. The Indiana cooperative remote search task (CReST) corpus. In Proceedings of the Language Resources and Evaluation Conference (LREC\u201910)."},{"key":"e_1_3_2_18_2","volume-title":"Proceedings of the Annual Conference on Advances in Cognitive Systems","author":"English Jesse","year":"2020","unstructured":"Jesse English and Sergei Nirenburg. 2020. OntoAgent: Implementing content-centric cognitive models. In Proceedings of the Annual Conference on Advances in Cognitive Systems."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2021.674292"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/100.580977"},{"key":"e_1_3_2_21_2","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence","author":"Frasca Tyler","year":"2021","unstructured":"Tyler Frasca, Bradley Oosterveld, Meia Chita-Tegmark, and Matthias Scheutz. 2021. Enabling fast instruction-based modification of learned robot skills. In Proceedings of the 35th AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_22_2","article-title":"Using natural language for reward shaping in reinforcement learning","author":"Goyal Prasoon","year":"2019","unstructured":"Prasoon Goyal, Scott Niekum, and Raymond J. Mooney. 2019. Using natural language for reward shaping in reinforcement learning. Retrieved from https:\/\/arXiv:1903.02020","journal-title":"R"},{"issue":"2","key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2514\/1.37056","article-title":"Control of mobile robots using the soar cognitive architecture","volume":"6","author":"Hanford Scott D.","year":"2009","unstructured":"Scott D. Hanford, Oranuj Janrathitikarn, and Lyle N. Long. 2009. Control of mobile robots using the soar cognitive architecture. J. Aerospace Comput. Info. Commun. 6, 2 (2009), 69\u201391.","journal-title":"J. Aerospace Comput. Info. Commun."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.5555\/973226.973229"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v22i3.1572"},{"key":"e_1_3_2_26_2","first-page":"4569","volume-title":"Proceedings of the IEEE International Conference on Robotics and Automation","author":"Kalakrishnan Mrinal","year":"2011","unstructured":"Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, Peter Pastor, and Stefan Schaal. 2011. STOMP: Stochastic trajectory optimization for motion planning. In Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, 4569\u20134574."},{"key":"e_1_3_2_27_2","first-page":"995","volume-title":"Proceedings of the Millennium Conference: IEEE International Conference on Robotics and Automation (Cat. No. 00CH37065)","volume":"2","author":"Kuffner James J.","year":"2000","unstructured":"James J. Kuffner and Steven M. LaValle. 2000. RRT-connect: An efficient approach to single-query path planning. In Proceedings of the Millennium Conference: IEEE International Conference on Robotics and Automation (Cat. No. 00CH37065), Vol. 2. IEEE, 995\u20131001."},{"key":"e_1_3_2_28_2","volume-title":"The Soar Cognitive Architecture","author":"Laird John E.","year":"2019","unstructured":"John E. Laird. 2019. The Soar Cognitive Architecture. MIT Press."},{"key":"e_1_3_2_29_2","volume-title":"Proceedings of the AAAI workshop on Cognitive Robotics (CogRob@AAAI\u201912)","author":"Laird John Edwin","year":"2012","unstructured":"John Edwin Laird, Keegan R. Kinkade, Shiwali Mohan, and Joseph Z. Xu. 2012. Cognitive robotics using the Soar cognitive architecture. In Proceedings of the AAAI workshop on Cognitive Robotics (CogRob@AAAI\u201912). Citeseer."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116249"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/122344.122365"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/29.45616"},{"key":"e_1_3_2_33_2","first-page":"57","volume-title":"Proceedings of the 5th Workshop on Formal Semantics and Pragmatics of Dialogue (Bi-Dialog\u201901)","author":"Lemon Oliver","year":"2001","unstructured":"Oliver Lemon, Anne Bracy, Alexander Gruenstein, and Stanley Peters. 2001. Information states in a multi-modal dialogue system for human-robot conversation. In Proceedings of the 5th Workshop on Formal Semantics and Pragmatics of Dialogue (Bi-Dialog\u201901). Citeseer, 57\u201367."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/1017494.1017496"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0277(83)90026-4"},{"key":"e_1_3_2_36_2","article-title":"HCRC Disfluency Coding Manual","author":"Lickley Robin J.","year":"1998","unstructured":"Robin J. Lickley. 1998. HCRC Disfluency Coding Manual. Human Communication Research Centre, University of Edinburgh.","journal-title":"Human Communication Research Centre, University of Edinburgh"},{"key":"e_1_3_2_37_2","first-page":"123","volume-title":"Proceedings of the 2nd Conference on Robot Learning (Proceedings of Machine Learning Research)","volume":"87","author":"Losey Dylan P.","year":"2018","unstructured":"Dylan P. Losey and Marcia K. O\u2019Malley. 2018. Including uncertainty when learning from human corrections. In Proceedings of the 2nd Conference on Robot Learning (Proceedings of Machine Learning Research), Aude Billard, Anca Dragan, Jan Peters, and Jun Morimoto (Eds.), Vol. 87. PMLR, 123\u2013132. Retrieved from https:\/\/proceedings.mlr.press\/v87\/losey18a.html"},{"key":"e_1_3_2_38_2","article-title":"Improving disfluency detection by self-training a self-attentive model","author":"Lou Paria Jamshid","year":"2020","unstructured":"Paria Jamshid Lou and Mark Johnson. 2020. Improving disfluency detection by self-training a self-attentive model. Retrieved from https:\/\/arXiv:2004.05323","journal-title":"R"},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Jelena Luketina Nantas Nardelli Gregory Farquhar Jakob Foerster Jacob Andreas Edward Grefenstette Shimon Whiteson and Tim Rockt\u00e4schel. 2019. A survey of reinforcement learning informed by natural language. Retrieved from https:\/\/arXiv:1906.03926","DOI":"10.24963\/ijcai.2019\/880"},{"key":"e_1_3_2_40_2","unstructured":"Corey Lynch Ayzaan Wahid Jonathan Tompson Tianli Ding James Betker Robert Baruch Travis Armstrong and Pete Florence. 2022. Interactive language: Talking to robots in real time. Retrieved from https:\/\/arXiv:2210.06407"},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/978-3-319-00065-7_28","volume-title":"Proceedings of the 13th International Symposium on Experimental Robotics","author":"Matuszek Cynthia","year":"2013","unstructured":"Cynthia Matuszek, Evan Herbst, Luke Zettlemoyer, and Dieter Fox. 2013. Learning to parse natural language commands to a robot control system. In Proceedings of the 13th International Symposium on Experimental Robotics. Springer, 403\u2013415."},{"key":"e_1_3_2_42_2","first-page":"89","volume-title":"Proceedings of the 5th Workshop on Disfluency in Spontaneous Speech","author":"Nicholson H.","year":"2010","unstructured":"H. Nicholson, K. Eberhard, and M. Scheutz. 2010. Um...I don\u2019t see any: The function of filled pauses and repairs. In Proceedings of the 5th Workshop on Disfluency in Spontaneous Speech. 89\u201392."},{"key":"e_1_3_2_43_2","volume-title":"Proceedings of the AAAI Fall Symposium on Natural Communication with Robots","author":"Nirenburg Sergei","year":"2017","unstructured":"Sergei Nirenburg and Peter Wood. 2017. Toward human-style learning in robots. In Proceedings of the AAAI Fall Symposium on Natural Communication with Robots."},{"key":"e_1_3_2_44_2","first-page":"6964","volume-title":"Proceedings of the International Conference on Robotics and Automation (ICRA\u201919)","author":"Park Jae Sung","year":"2019","unstructured":"Jae Sung Park, Biao Jia, Mohit Bansal, and Dinesh Manocha. 2019. Efficient generation of motion plans from attribute-based natural language instructions using dynamic constraint mapping. In Proceedings of the International Conference on Robotics and Automation (ICRA\u201919). IEEE, 6964\u20136971."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2014.968093"},{"key":"e_1_3_2_46_2","first-page":"5","volume-title":"Proceedings of the ICRA Workshop on Open Source Software","volume":"3","author":"Quigley Morgan","year":"2009","unstructured":"Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, Andrew Y. Ng et\u00a0al. 2009. ROS: An open-source robot operating system. In Proceedings of the ICRA Workshop on Open Source Software, Vol. 3. Kobe, Japan, 5."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1002\/wcs.1488"},{"key":"e_1_3_2_48_2","unstructured":"Frank R\u00f6der and Manfred Eppe. 2022. Language-conditioned reinforcement learning to solve misunderstandings with action corrections. Retrieved from https:\/\/arXiv:2211.10168"},{"key":"e_1_3_2_49_2","volume-title":"In Proceedings of the AAAI Robot Workshop","author":"Schermerhorn P.","year":"2006","unstructured":"P. Schermerhorn, J. Kramer, T. Brick, D. Anderson, A. Dingler, and M. Scheutz. 2006. Diarc: A testbed for natural human-robot interactions. In In Proceedings of the AAAI Robot Workshop. AAAI Press."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v32i4.2381"},{"key":"e_1_3_2_51_2","first-page":"277","article-title":"Systematic integration of cognitive and robotic architectures","volume":"2","author":"Scheutz Matthias","year":"2013","unstructured":"Matthias Scheutz, Jack Harris, and Paul Schermerhorn. 2013. Systematic integration of cognitive and robotic architectures. Adv. Cogn. Syst. 2 (2013), 277\u2013296.","journal-title":"Adv. Cogn. Syst."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.5555\/3091125.3091315"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.5555\/3091125.3091315"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-006-9018-3"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/978-3-319-97550-4_11","article-title":"An overview of the distributed integrated cognition affect and reflection DIARC architecture","author":"Scheutz Matthias","year":"2019","unstructured":"Matthias Scheutz, Thomas Williams, Evan Krause, Bradley Oosterveld, Vasanth Sarathy, and Tyler Frasca. 2019. An overview of the distributed integrated cognition affect and reflection DIARC architecture. Cogn. Architect. (2019), 165\u2013193.","journal-title":"Cogn. Architect."},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"Pratyusha Sharma Balakumar Sundaralingam Valts Blukis Chris Paxton Tucker Hermans Antonio Torralba Jacob Andreas and Dieter Fox. 2022. Correcting robot plans with natural language feedback. Retrieved from https:\/\/arXiv:2204.05186","DOI":"10.15607\/RSS.2022.XVIII.065"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4313"},{"key":"e_1_3_2_58_2","volume-title":"Proceedings of the 5th European Conference on Speech Communication and Technology","author":"Shriberg Elizabeth","year":"1997","unstructured":"Elizabeth Shriberg, Rebecca Bates, and Andreas Stolcke. 1997. A prosody only decision-tree model for disfluency detection. In Proceedings of the 5th European Conference on Speech Communication and Technology."},{"key":"e_1_3_2_59_2","volume-title":"Proceedings of the Conference on Robotics: Science and Systems","author":"Squire Shawn","year":"2015","unstructured":"Shawn Squire, Stefanie Tellex, Dilip Arumugam, and Lei Yang. 2015. Grounding English commands to reward functions. In Proceedings of the Conference on Robotics: Science and Systems."},{"issue":"3","key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev-control-101119-071628","article-title":"Robots that use language","author":"Tellex Stefanie","year":"2020","unstructured":"Stefanie Tellex, Nakul Gopalan, Hadas Kress-Gazit, and Cynthia Matuszek. 2020. Robots that use language. Annu. Rev. Control, Robot. Auton. Syst.3 (2020), 25\u201355.","journal-title":"Annu. Rev. Control, Robot. Auton. Syst."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.5898\/JHRI.2.1.Trafton"},{"key":"e_1_3_2_62_2","first-page":"2785","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Wang Shaolei","year":"2017","unstructured":"Shaolei Wang, Wanxiang Che, Yue Zhang, Meishan Zhang, and Ting Liu. 2017. Transition-based disfluency detection using lstms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2785\u20132794."},{"key":"e_1_3_2_63_2","volume-title":"Proceedings of the Workshop on Autonomous Mobile Service Robots","author":"Wise Melonee","year":"2016","unstructured":"Melonee Wise, Michael Ferguson, Derek King, Eric Diehr, and David Dymesich. 2016. Fetch and freight: Standard platforms for service robot applications. In Proceedings of the Workshop on Autonomous Mobile Service Robots."},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","unstructured":"Vicky Zayats Mari Ostendorf and Hannaneh Hajishirzi. 2016. Disfluency detection using a bidirectional LSTM. Retrieved from https:\/\/arXiv:1604.03209","DOI":"10.21437\/Interspeech.2016-1247"}],"container-title":["ACM Transactions on Human-Robot Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3623385","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3623385","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:26Z","timestamp":1750178186000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3623385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,26]]},"references-count":63,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3623385"],"URL":"https:\/\/doi.org\/10.1145\/3623385","relation":{},"ISSN":["2573-9522"],"issn-type":[{"value":"2573-9522","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,26]]},"assertion":[{"value":"2022-06-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}