Robotic Process Automation Activity Recommendation using NLP Techniques

The integration of Natural Language Processing (NLP) into Robotic Process Automation (RPA) has revolutionized the automation of complex business processes by addressing challenges such as interpreting unstructured data and reducing human intervention. This research focuses on enhancing RPA systems by developing NLP models to recommend UiPath activities based on textual process descriptions. Leveraging pre-trained transformer models, including BERT, DistilBERT, TinyBERT, and ALBERT, fine-tuned on a structured and diverse dataset, the study incorporates linguistic variability to improve generalization. A robust evaluation framework based on precision, recall, and F1-score underscores the performance of these models. Key contributions include the creation of high-accuracy NLP models, a comprehensive evaluation methodology, and a representative dataset designed for UiPath activity recommendation. The findings highlight NLP's potential to streamline automation workflows, reduce manual effort, and enhance scalability, ultimately bridging the gap between unstructured text and automation tasks, and laying the groundwork for advanced, autonomous RPA systems.