In the financial industry, identifying user intent from text inputs is crucial for various tasks such as automated trading, sentiment analysis, and customer support. One important component of natural language processing (NLP) is intent detection, which is significant to the finance sector. Limited studies have been conducted in the field of finance using languages with limited resources like Arabic, despite notable works being done in high-resource languages like English. To advance Arabic NLP in the financial domain, the organizer of AraFinNLP 2024 has arranged a shared task for detecting banking intents from the queries in various Arabic dialects, introducing a novel dataset named ArBanking77 which includes a collection of banking queries categorized into 77 distinct intents classes. To accomplish this task, we have presented a hierarchical approach called Dual-Phase-BERT in which the detection of dialects is carried out first, followed by the detection of banking intents. Using the provided ArBanking77 dataset, we have trained and evaluated several conventional machine learning, and deep learning models along with some cutting-edge transformer-based models. Among these models, our proposed Dual-Phase-BERT model has ranked 7^th out of all competitors, scoring 0.801 on the scale of F1-score on the test set.

Predicting semantic textual relatedness (STR) is one of the most challenging tasks in the field of natural language processing. Semantic relatedness prediction has real-life practical applications while developing search engines and modern text generation systems. A shared task on semantic textual relatedness has been organized by SemEval 2024, where the organizer has proposed a dataset on semantic textual relatedness in the English language under Shared Task 1 (Track A3). In this work, we have developed models to predict semantic textual relatedness between pairs of English sentences by training and evaluating various transformer-based model architectures, deep learning, and machine learning methods using the shared dataset. Moreover, we have utilized existing semantic textual relatedness datasets such as the stsb multilingual benchmark dataset, the SemEval 2014 Task 1 dataset, and the SemEval 2015 Task 2 dataset. Our findings show that in the SemEval 2024 Shared Task 1 (Track A3), the fine-tuned-STS-BERT model performed the best, scoring 0.8103 on the test set and placing 25th out of all participants.