@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Persian Abstract Meaning Representation: Annotation Guidelines and Gold Standard Dataset
%A Takhshid, Reza
%A Azin, Tara
%A Shojaei, Razieh
%A Bahrani, Mohammad
%Y Xue, Nianwen
%Y Martin, James
%S Proceedings of the 2024 UMR Parsing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Boulder, Colorado
%F takhshid-etal-2024-persian
%X 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.
%U https://aclanthology.org/2024.umrpw-1.2/
%P 8-15
Markdown (Informal)
[Persian Abstract Meaning Representation: Annotation Guidelines and Gold Standard Dataset](https://aclanthology.org/2024.umrpw-1.2/) (Takhshid et al., UMRPW 2024)
ACL