Lecture image placeholder

Premium content

Access to this content requires a subscription. You must be a premium user to view this content.

Monthly subscription - $9.99Pay per view - $4.99Access through your institutionLogin with Underline account
Need help?
Contact us
Lecture placeholder background
VIDEO DOI: https://doi.org/10.48448/rk2n-n798

workshop paper

ACL 2024

August 16, 2024

Bangkok, Thailand

MA at AraFinNLP2024: BERT-based Ensemble for Cross-dialectal Arabic Intent Detection

keywords:

contrastive loss

ensemble

intent classification

Intent detection, also called intent classification or recognition, is an NLP technique to comprehend the purpose behind user utterances. This paper focuses on Multi-dialect Arabic intent detection in banking, utilizing the ArBanking77 dataset. Our method employs an ensemble of fine-tuned BERT-based models, integrating contrastive loss for training. To enhance generalization to diverse Arabic dialects, we augment the ArBanking77 dataset, originally in Modern Standard Arabic (MSA) and Palestinian, with additional dialects such as Egyptian, Moroccan, and Saudi, among others. Our approach achieved an F1-score of 0.8771, ranking first in subtask-1 of the AraFinNLP shared task 2024.

Downloads

Transcript English (automatic)

Next from ACL 2024

BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification
workshop paper

BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification

ACL 2024

Nsrin Ashraf

16 August 2024

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)

PRESENTATIONS

  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved