A Natural Language Processing Model for the Development of an Italian-Language Chatbot for Public Administration

Natural Language Processing models (NLP) are used in chatbots to understand user input, interpret its meaning, and generate conversational responses to provide immediate and consistent assistance. This reduces problem-solving time and staff workload and increases user satisfaction. There are both rule- based chatbots, which use decision trees and are programmed to answer specific questions, and self-learning chatbots, which can handle more complex conversations through continuous learning about data and user interactions. However, only a few chatbots have been developed specifically for the Italian language. T he development of chatbots for Public Administration (PA) in the Italian language presents unique challenges, particularly in creating models that can accurately understand and respond to user queries based on complex, context-specific documents. This paper proposes a novel natural language processing (NLP) model tailored to the Italian language, designed to support the development of an advanced Question Answering (QA) chatbot for PA. The core of the proposed model is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, enhanced with an encoder/decoder module and a highway network module to improve the filtering and processing of input text. The principal aim of this research is to address the gap in Italian-language NLP models by providing a robust solution capable of handling the intricacies of the Italian language within the context of PA. The model is trained and evaluated using the Italian version of the Stanford Question Answering Dataset (SQuAD-IT). Experimental results demonstrate that the proposed model outperforms existing models such as BIDAF in terms of F1-score and Exact Match (EM), indicating its superior ability to provide precise and accurate answers. The comparative analysis highlights a significant performance improvement, with the proposed model achieving an F1-score of 59.41% and an EM of 46.24%, compared to 49.35% and 38.43%, respectively, for BIDAF. The findings suggest that the proposed model offers substantial benefits in terms of accuracy and efficiency for PA applications.