Persian Abstract Meaning Representation: Annotation Guidelines and Gold Standard Dataset

2022-05-16

AI Summary

This work addresses the scarcity of Abstract Meaning Representation (AMR) resources for Persian, a low-resource language. Methodologically, we adapt the AMR v3.0 framework to Persian’s rich morphology and flexible word order through linguistically motivated, language-specific extensions; design a cross-lingual alignment–guided collaborative annotation protocol; and integrate expert validation throughout the process. Our contributions are threefold: (1) the first publicly available, reproducible Persian AMR annotation guideline; (2) the first high-quality, manually verified Persian AMR corpus (1,562 sentences); and (3) empirical insights into the adaptation challenges—and necessary structural reforms—of universal semantic representation frameworks for highly inflected, free-word-order languages. This resource enables downstream research in Persian semantic parsing, machine translation, and cross-lingual AMR parsing.

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 1,562 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.

Problem

• Adapting AMR annotation for Persian syntax structures
• Developing Persian AMR gold standard dataset
• Addressing challenges in universal meaning representation

Innovation

• Adapting AMR guidelines for Persian syntax
• Creating Persian AMR gold standard dataset
• Addressing challenges in universal meaning representation