Persian Abstract Meaning Representation: Annotation Guidelines and Gold Standard Dataset
This work addresses the need for semantic annotation resources in Persian, a low-resource language, but is incremental as it adapts an existing framework rather than introducing new methods.
The paper tackles the challenge of adapting Abstract Meaning Representation (AMR) to Persian by developing annotation guidelines and a gold standard dataset of 1,562 sentences, highlighting the difficulties in creating a universal framework for low-resource languages.
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.