Amharic chatbot on Ethiopian civil code law using a deep learning approach

Bizuayehu, Tadege

dc.contributor.author	Bizuayehu, Tadege
dc.date.accessioned	2023-06-19T11:20:02Z
dc.date.available	2023-06-19T11:20:02Z
dc.date.issued	2022-12
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/15401
dc.description.abstract	A chatbot is a computer program used to interact between humans and computer systems using natural language processing via speech or text. In this research, we developed Amharic chatbots that support lawyers and other users of chatbots in assisting with civil codes i.e., about people's rights and inheritance rights. The majority of the population either do not afford to hire a lawyer or do not know the civil code. As a result, this study aims to develop an Amharic chatbot that can support individuals, lawyers, and judges in making legal decisions. We used an experimental research methodology to develop a model for civil code laws in this research. In designing a model, we used RNN (LSTM and BiLSTM), and transformer encoder-decoder chatbot in training and testing a model. We used BLEU score metrics and user acceptance testing to measure the model's performance. The BLEU score measurement technique measures the similarity between the target civil code answer and the model-predicted civil code answers on the user's query. After experimenting with each model with different hyperparameters, we got a BLEU score of 9.88% in the transformer model. Compared with the LSTM and BiLSTM models, the transformer model achieves good performance on training time, memory usage, and prediction accuracy. Generally, the design model responds to the user's query based on the learned weight in training. Improving the performance of a model for the transformer model, the dataset size, and different user utterances involvement remains a challenge. Keywords: -Amharic language, LSTM, BiLSTM, chatbot, conversational agent	en_US
dc.language.iso	en_US	en_US
dc.subject	Computing	en_US
dc.title	Amharic chatbot on Ethiopian civil code law using a deep learning approach	en_US
dc.type	Thesis	en_US