@inproceedings{lamarche-langlais-2024-benchie,
    title = "{B}ench{IE}{\textasciicircum}{FL}: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark",
    author = "Lamarche, Fabrice  and
      Langlais, Philippe",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-acl.496/",
    doi = "10.18653/v1/2024.findings-acl.496",
    pages = "8372--8394",
    abstract = "Open Information Extraction (OIE) is a field of natural language processing that aims to present textual information in a format that allows it to be organized, analyzed and reflected upon. Numerous OIE systems are developed, claiming ever-increasing performance, marking the need for objective benchmarks. BenchIE is the latest reference we know of. Despite being very well thought out, we noticed a number of issues we believe are limiting. Therefore, we propose BenchIE{\textasciicircum}FL, a new OIE benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. BenchIE{\textasciicircum}FL allows insightful conclusions to be drawn on the actual performance of OIE extractors."
}