Related papers: Deep Learning Based Amharic Chatbot for FAQs in Universities

Deep Learning Based Amharic Chatbot for FAQs in Universities

URL: http://arxiv.org/abs/2402.01720v2
Date: Tue, 05 Nov 2024 18:45:22 GMT
Title: Deep Learning Based Amharic Chatbot for FAQs in Universities
Authors: Goitom Ybrah Hailu, Hadush Hailu, Shishay Welay,
Abstract summary: This paper proposes a model that answers frequently asked questions (FAQs) in the Amharic language. The proposed program employs tokenization, stop word removal, and stemming to analyze and categorize Amharic input sentences. The model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility.
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Abstract: University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer programs that simulate human conversation through the use of artificial intelligence (AI), acting as a virtual assistant to handle questions and other tasks. The proposed chatbot program employs tokenization, normalization, stop word removal, and stemming to analyze and categorize Amharic input sentences. Three machine learning model algorithms were used to classify tokens and retrieve appropriate responses: Support Vector Machine (SVM), Multinomial Na\"ive Bayes, and deep neural networks implemented through TensorFlow, Keras, and NLTK. The deep learning model achieved the best results with 91.55% accuracy and a validation loss of 0.3548 using an Adam optimizer and SoftMax activation function. The chatbot model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility. The experimental results demonstrate that the chatbot framework achieved its objectives and effectively addressed challenges such as Amharic Fidel variation, morphological variation, and lexical gaps. Future research could explore the integration of Amharic WordNet to narrow the lexical gap and support more complex questions.

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