@inproceedings{lichouri-etal-2024-dzfinnlp,
    title = "dz{F}in{N}lp at {A}ra{F}in{NLP}: Improving Intent Detection in Financial Conversational Agents",
    author = "Lichouri, Mohamed  and
      Lounnas, Khaled  and
      Zakaria, Amziane",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Eskander, Ramy  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Abdelali, Ahmed  and
      Touileb, Samia  and
      Hamed, Injy  and
      Onaizan, Yaser  and
      Alhafni, Bashar  and
      Antoun, Wissam  and
      Khalifa, Salam  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Mrini, Khalil",
    booktitle = "Proceedings of the Second Arabic Natural Language Processing Conference",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.arabicnlp-1.43/",
    doi = "10.18653/v1/2024.arabicnlp-1.43",
    pages = "450--455",
    abstract = "In this paper, we present our dzFinNlp team{'}s contribution for intent detection in financial conversational agents, as part of the AraFinNLP shared task. We experimented with various models and feature configurations, including traditional machine learning methods like LinearSVC with TF-IDF, as well as deep learning models like Long Short-Term Memory (LSTM). Additionally, we explored the use of transformer-based models for this task. Our experiments show promising results, with our best model achieving a micro F1-score of 93.02{\%} and 67.21{\%} on the ArBanking77 dataset, in the development and test sets, respectively."
}