Deep Learning Based Amharic Chatbot for FAQs in Universities
Goitom Ybrah Hailu Shishay Welay
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.