In this paper, a description of the system submitted by BFCAI team to the AraFinNLP 2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines out-performed all other algorithms with an f-score of 49%. Our submission's result is appropriate compared to the simplicity of the proposed model's structure.

Dialect identification task aims at detecting the source variant of a given text or speech segment automatically. Nuanced Arabic Dialect Identification (NADI) shared task 2022 has two subtasks, the first subtask for country-level identification and the second subtask for sentiment analysis. Our team participated in the first subtask.

This paper describes the systems submitted by BFCAI team to Nuanced Arabic Dialect Identification (NADI) shared task 2022. Dialect identification task aims at detecting the source variant of a given text or speech segment automatically. There are two subtasks in NADI 2022, the first subtask for country-level identification and the second subtask for sentiment analysis. Our team participated in the first subtask. The proposed systems use Term Frequency Inverse/Document Frequency and word em-beddings as vectorization models. Different machine learning algorithms have been used as classifiers. The proposed systems have been tested on two test sets: Test-A and Test-B. The proposed models achieved Macro-f1 score of 21.25% and 9.71% for Test-A and Test-B set respectively. On other hand, the best-performed submitted system achieved Macro-f1 score of 36.48% and 18.95% for Test-A and Test-B set respectively .

This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results.