Intent Recognition Leveraging XLM-RoBERTa for Effective NLU

Natural Language Processing (NLP) systems are vital for precisely interpreting user intentions in order to guarantee efficient communication between people and computers. Intent recognition is a crucial component of natural language processing (NLP), and the XLM-RoBERTa (Cross-lingual Multimodel-Robust Optimized Bidirectional Representation of Transformer Encoder) model is tested in this work by using the ATIS dataset. Findings show that by spotting subtle patterns in the dataset and making use of contextual information, the model improves intent detection with an amazing 98.57% accuracy and efficiency. Together with examining the effects of language variety and dataset size on model performance, the study offers insights into anticipating interpretability and typical problems with intent identification. The study’s practical importance is shown by the creation of a web application designed for real-world situations. The study’s overall findings demonstrate how flexible sophisticated natural language processing (NLP) models, such as XLM-RoBERTa, can be to provide insightful information on intent recognition, enhancing user experiences and propelling NLP research. In order to maximize human-computer communication efficiency and promote innovation in the industry, it emphasizes the significance of incorporating cutting-edge NLP approaches into useful applications.