dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916329578037248 |
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| author | Lichouri, Mohamed Lounnas, Khaled Amziane, Mohamed Zakaria |
| author_facet | Lichouri, Mohamed Lounnas, Khaled Amziane, Mohamed Zakaria |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_13565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents Lichouri, Mohamed Lounnas, Khaled Amziane, Mohamed Zakaria Computation and Language 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. |
| title | dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.13565 |