A deep learning architecture for emotional aware chatbots
Year: 2020
Type: dissertation
Abstract: Chatbots cover a broad range of possible applications. Interacting with human emotions is a small but necessary subset of the skillset necessary for meaningful interaction. This paper explores strategies to create a chatbot that is able to adapt to the emotional cues during a conversation. A first challenge is to handle the dimensionality of human communication that is addressed with strategies to reduce the dimensionality of both input and output. Additional challenges are handling the continuous action space of semantic vectors and the variations in personality types. I propose an ensemble model of machine learning techniques and conclude with a perspective on the generation of meaning and the consequences this has for a possible implementation
Author: R.H. Grouls
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3113448344