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Chatbot Based on Artificial Neural Networks
Čechák, Jiří ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
The thesis describes an implementation and the way generative chatbot operates. The chatbot was implemented in Python using artificial neural networks and is based on a sequence-to-sequence principle. The final chatbot contains three models, which can be trained and used for conversations in a created GUI. After training of all three models, the chatbot was then tested by using BLEU metric. It was also tested by some users who compared the quality of its generated answers with the quality of answers created by already an existing chatbot Cleverbot. For a better understanding of the given problematics, there is a simple description of the basic terms, such as artificial intelligence, artificial neural networks, the difference between closed and open domain, word embedding and a basic description of the chatbots and their types, including their advantages, disadvantages and usage.
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Chatbot Based on Artificial Neural Networks
Čechák, Jiří ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
The thesis describes an implementation and the way generative chatbot operates. The chatbot was implemented in Python using artificial neural networks and is based on a sequence-to-sequence principle. The final chatbot contains three models, which can be trained and used for conversations in a created GUI. After training of all three models, the chatbot was then tested by using BLEU metric. It was also tested by some users who compared the quality of its generated answers with the quality of answers created by already an existing chatbot Cleverbot. For a better understanding of the given problematics, there is a simple description of the basic terms, such as artificial intelligence, artificial neural networks, the difference between closed and open domain, word embedding and a basic description of the chatbots and their types, including their advantages, disadvantages and usage.
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