@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents
%A Lichouri, Mohamed
%A Lounnas, Khaled
%A Zakaria, Amziane
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lichouri-etal-2024-dzfinnlp
%X 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.
%U https://aclanthology.org/2024.arabicnlp-1.43
%P 450-455
Markdown (Informal)
[dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents](https://aclanthology.org/2024.arabicnlp-1.43) (Lichouri et al., ArabicNLP-WS 2024)
ACL