{"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/maven-arg-completing-the-puzzle-of-all-in-one","title":"MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation","arxiv_id":"2311.09105","date":"2023-11-15","proceeding":null,"authors":["Xiaozhi Wang","Hao Peng","Yong Guan","Kaisheng Zeng","Jianhui Chen","Lei Hou","Xu Han","Yankai Lin","Zhiyuan Liu","Ruobing Xie","Jie zhou","Juanzi Li"],"abstract":"Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and codes can be obtained from https://github.com/THU-KEG/MAVEN-Argument.","url_abs":"https://arxiv.org/abs/2311.09105v2","url_pdf":"https://arxiv.org/pdf/2311.09105v2.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":"maven-arg-completing-the-puzzle-of-all-in-one","repo_url":"https://github.com/thu-keg/maven-argument","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"event-argument-extraction","task_name":"Event Argument Extraction"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"event-relation-extraction","task_name":"Event Relation Extraction"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"}],"methods":[],"datasets_introduced":[{"slug":"maven-arg","name":"MAVEN-Arg","full_name":"MAVEN-Arguments"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2311.09105","atlas_url":"https://app.syntology.ai/?focus=2311.09105","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}