ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task

TL;DR

The ArabicNLU 2024 shared task introduced new datasets and benchmarks for evaluating Arabic word sense disambiguation and location mention disambiguation, fostering progress in Arabic natural language understanding.

Contribution

This paper presents the first shared task on Arabic NLU, including novel datasets and evaluation benchmarks for WSD and LMD tasks.

Findings

Highest WSD accuracy was 77.8%.

Highest LMD MRR@1 was 95.0%.

Limited team participation highlights challenges in Arabic NLU.

Abstract

This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location Mention Disambiguation (LMD). The task aimed to evaluate the ability of automated systems to resolve word ambiguity and identify locations mentioned in Arabic text. We provided participants with novel datasets, including a sense-annotated corpus for WSD, called SALMA with approximately 34k annotated tokens, and the IDRISI-DA dataset with 3,893 annotations and 763 unique location mentions. These are challenging tasks. Out of the 38 registered teams, only three teams participated in the final evaluation phase, with the highest accuracy being 77.8% for WSD and the highest MRR@1 being 95.0% for LMD. The shared task not only facilitated the evaluation and comparison of different techniques, but also provided valuable insights and resources for the continued advancement of Arabic NLU technologies.