{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/how-good-is-the-model-in-model-in-the-loop","title":"How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?","arxiv_id":"2306.05434","date":"2023-06-06","proceeding":null,"authors":["Shafiuddin Rehan Ahmed","Abhijnan Nath","Michael Regan","Adam Pollins","Nikhil Krishnaswamy","James H. Martin"],"abstract":"Annotating cross-document event coreference links is a time-consuming and cognitively demanding task that can compromise annotation quality and efficiency. To address this, we propose a model-in-the-loop annotation approach for event coreference resolution, where a machine learning model suggests likely corefering event pairs only. We evaluate the effectiveness of this approach by first simulating the annotation process and then, using a novel annotator-centric Recall-Annotation effort trade-off metric, we compare the results of various underlying models and datasets. We finally present a method for obtaining 97\\% recall while substantially reducing the workload required by a fully manual annotation process. Code and data can be found at https://github.com/ahmeshaf/model_in_coref","url_abs":"https://arxiv.org/abs/2306.05434v1","url_pdf":"https://arxiv.org/pdf/2306.05434v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"how-good-is-the-model-in-model-in-the-loop","repo_url":"https://github.com/ahmeshaf/model_in_coref","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"coreference-resolution","task_name":"Coreference Resolution"},{"task_slug":"event-coreference-resolution","task_name":"Event Coreference Resolution"},{"task_slug":"coreference-resolution-1","task_name":"coreference-resolution"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2306.05434","atlas_url":"https://app.syntology.ai/?focus=2306.05434","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}