@inproceedings{takhshid-etal-2024-persian,
    title = "{P}ersian {A}bstract {M}eaning {R}epresentation: Annotation Guidelines and Gold Standard Dataset",
    author = "Takhshid, Reza  and
      Azin, Tara  and
      Shojaei, Razieh  and
      Bahrani, Mohammad",
    editor = "Xue, Nianwen  and
      Martin, James",
    booktitle = "Proceedings of the 2024 UMR Parsing Workshop",
    month = jun,
    year = "2024",
    address = "Boulder, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2024.umrpw-1.2/",
    pages = "8--15",
    abstract = "This paper introduces the Persian Abstract Meaning Representation (AMR) guidelines, a detailed guide for annotating Persian sentences with AMR, focusing on the necessary adaptations to fit Persian{'}s unique syntactic structures. We discuss the development process of a Persian AMR gold standard dataset consisting of 1562 sentences created following the guidelines. By examining the language specifications and nuances that distinguish AMR annotations of a low-resource language like Persian, we shed light on the challenges and limitations of developing a universal meaning representation framework. The guidelines and the dataset introduced in this study highlight such challenges, aiming to advance the field."
}