AraFinNLP 2024: The First Arabic Financial NLP Shared Task

TL;DR

The AraFinNLP 2024 shared task advances Arabic financial NLP by evaluating intent detection and translation across dialects using a large, annotated dataset, fostering development in banking chatbots and machine translation.

Contribution

This is the first shared task focusing on Arabic financial NLP, introducing a new dataset and benchmarking dialectal intent detection and translation methods.

Findings

High F1 score of 0.8773 in intent detection

BLEU score of 1.667 in cross-dialect translation

Active participation from 11 teams in intent detection

Abstract

The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (ii) Cross-dialect Translation and Intent Preservation. This shared task uses the updated ArBanking77 dataset, which includes about 39k parallel queries in MSA and four dialects. Each query is labeled with one or more of a common 77 intents in the banking domain. These resources aim to foster the development of robust financial Arabic NLP, particularly in the areas of machine translation and banking chat-bots. A total of 45 unique teams registered for this shared task, with 11 of them actively participated in the test phase. Specifically, 11 teams participated in Subtask 1, while only 1 team participated in Subtask 2. The winning team of Subtask 1 achieved F1 score of 0.8773, and the only team submitted in Subtask 2 achieved a 1.667 BLEU score.