A Sentence-Level Encoder–Decoder Architecture for Designing an Administrative Roman Urdu Chatbot - Muhammad Nazam Maqbool; Rana Muhammad Saleem; Nadeem Sarwar; Muhammad Ibrahim; Muhammad Shadab Alam Hashmi (Applied Computational Intelligence and Soft Computing)
This research focuses on developing an intelligent administrative chatbot for Roman Urdu to overcome the language barrier that hinders individuals who are not fluent in English from utilizing existing chatbot frameworks. While chatbot architectures can be rule based or artificial intelligence (AI) based, the core objective of a chatbot is to effectively address user queries across diverse domains. AI-based chatbots have demonstrated higher interactivity, scalability, adaptability to human interests, and evolving knowledge compared with rule-based architectures. However, the majority of existing chatbot frameworks is in English, which poses a challenge in regions like Pakistan where only a small percentage of the population is proficient in English. In response to this challenge, this research introduces an intelligent administrative chatbot specifically developed for Roman Urdu. Roman Urdu is the practice of writing Urdu, the native language of Pakistan, using the English alphabet. The proposed chatbot allows students to post their questions in either English or Roman Urdu, enabling them to obtain conclusive answers. The chatbot system utilizes a sentence analysis approach to generate relevant and productive responses to user queries. The core building block of the proposed chatbot is the recurrent neural networks (RNNs) sequence-to-sequence (Seq2Seq) model with long short-term memory (LSTM) units. To train the model, a dataset was meticulously collected from various administrative offices at the University of Engineering and Technology (UET), Lahore, initially in English and subsequently translated into Roman Urdu using different writing styles. The effectiveness of the proposed system was evaluated through a human judgment approach, assessing the contextual relevance and productivity of the chatbot’s responses to relevant questions. In conclusion, this research aims to bridge the language gap in chatbot frameworks, enhancing accessibility and usability.