Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Abstract—Nowadays, advances in technology and medical science have led to significant changes in the field of healthcare. Consequently, an effort has been taken to develop an intelligent health assistant in the Persian language, focusing on the emergency department. To achieve this goal, a labeled dataset was prepared. Subsequently, an intelligent assistant architecture was developed, utilizing slot filling and speech act classification for natural language understanding. A dialogue manager was designed to address negation in patient statements, resulting in the classification of triage patients. Evaluation revealed that the assistant's performance matched that of emergency staff in 83% of cases.

Abstract—Nowadays, advances in technology and medical science have led to significant changes in the field of healthcare. Consequently, an effort has been taken to develop an intelligent health assistant in the Persian language, focusing on the emergency department. To achieve this goal, a labeled dataset was prepared. Subsequently, an intelligent assistant architecture was developed, utilizing slot filling and speech act classification for natural language understanding. A dialogue manager was designed to address negation in patient statements, resulting in the classification of triage patients. Evaluation revealed that the assistant's performance matched that of emergency staff in 83% of cases.

Abstract Abstract— Intent detection and slot filling are crucial for understanding human language and are essential for creating intelligent virtual assistants, chatbots, and other interactive systems that interpret user queries accurately. Recent advancements, especially in transformer-based architectures and large language models (LLMs), have significantly improved the effectiveness of intent detection and slot filling. This paper, proposes a method for effectively utilizing low volume fine-tuning data samples to enhance the natural language comprehension of lightweight language models, yielding a nimble and efficient approach. Our approach involves augmenting new data while increasing model layers to enhance understanding of desired intents and slots. We explored various synonym replacement methods and prompt-generated data samples created by large language models. To prevent semantic meaning disturbance, we established a lexical retention list containing non-O slots to preserve the sentence's core meaning. This strategy enhances the model's slot precision, recall, F1-score, and exact match metrics by 1.41%, 1.8%, 1.61%, and 3.81%, respectively, compared to not using it. The impact of increasing model layers was studied under different layer arrangement scenarios. Our results show that our proposed solution outperforms the baseline by 10.95% and 4.89% in exact match and slot F1-score evaluation metrics.