This Bachelor’s thesis will cover the end-to-end process of developing a personalized conversational interface for a specific domain, using Deep Learning techniques. In particular, it will focus on the study of the Dialog Manager module, which is in charge of deciding the next system response based on the current dialog state. AlthoughthereisplentyofliteratureaboutMachineLearningappliedtotheconstruction of dialog management models, there is very little reference to the utilization of Deep Learning for such task. As a result, this work analyzes the improvement that deep neural networks can bring to accuracy. Several models are created with TensorFlow, and comparisons are made with traditional Machine Learning solutions. Results show that Deep Learning is not the most recommended approach for this type of problems, yet further research is suggested for more complex datasets. After this, one of the Deep Learning models, based on a train scheduling domain, is used for the implementation of the dialog manager inside a real spoken dialog system. To integrate the rest of required components of such technology (automatic speech recognizer, natural language understanding module and text-to-speech service), a modern framework is used: DialogFlow. With this platform, a complete chatbot is built in the form of an assistant in the train scheduling domain. Evaluationof thespoken dialogsystemwith real users generatesavery positivefeedback, demonstrating that a Deep Learning based dialog manager is a valid solution in commercial conversational interfaces.Ingeniería Informátic
