Enhancing Intent Recognition for Mixed Script Queries Using Roman Transliteration

Intent identification has become a difficult problem given the rising usage of multilingual and mixed-script inquiries, especially in areas where Roman transliteration is widely employed. Traditional intent detection systems suffer from the discrepancies and differences in transliterated text, which lowers their accuracy. The objective of this paper is to examine difficulties connected with intent recognition in mixed-script inquiries and to create a method based on transliteration to enhance intent recognition to assess the efficacy of the suggested model concerning current intent detection methods. The suggested approach is feature extraction and classification using machine learning and deep learning models after Roman transliteration pre-processing of mixed-script queries. The proposed hybrid deep learning architecture, that involves CNN, BiLSTM, and an Attention mechanism, holds an accuracy of 92.4% and F1-score of 91.0%, and beats baseline models like SVM, Random Forest, LSTM, and Transformer. Moreover, transliteration preprocessing enhanced accuracy by 7–9% on various models, proving the success of the approach.