A Bi-LSTM and Attention Based Approach for Developing an Assamese AI Chatbot

Abstract

Objectives: The main objective of this study is to develop, evaluate, and analyses a Bi-LSTM and attention-based model and decide whether it is the best deep learning hybrid model for developing the Assamese AI Conversational Agent. Methods: A dataset for the Assamese language was created for this research. A bi-LSTM and attention-based model was developed and trained with the dataset. We have compared the accuracy and performance of the proposed model with those of other deep learning models. The bag of words technique was used for feature extraction of text. For natural language processing, a custom stemmer, stop word functions and root word and stop word database were developed. The performance of the model is measured using metrics such as precision, recall, and F1-score. Findings: From the research study, we have found that the bi-LSTM and attention-based model performed better in terms of all the metrics compared to other models. This model was able to attain an accuracy of 89.99%. Novelty: This research is novel as it is the first attempt to develop an AI chatbot in Assamese based on a deep learning model. Assamese is a language that has not received much attention in the field of AI chatbots. This research has great significance for the society where Assamese is spoken, as it can provide a platform for communication, information, and education in their native language. The Assamese language is in its beginning stages on the digital platform and has limited research done in the field of AI chatbots. This research will contribute a lot to this research area and open a new door for researchers who are interested in this topic. It will also allow them to further explore, improve, and develop new ideas. Keywords: Deep Learning, Natural language processing, Chatbot, Assamese