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VIDEO DOI: https://doi.org/10.48448/efp7-8x81

workshop paper

ACL 2024

August 16, 2024

Bangkok, Thailand

dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents

keywords:

arbanking77 dataset

long short-term memory (lstm)

linearsvc

financial conversational agents

tf-idf

transformer-based models

intent detection

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.

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Transcript English (automatic)

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