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Learning face marks for natural language dialogue systems

Face marks figures of faces which consist of characters such as (¿¿), and are effective for expressing emotions in a text-dialogue system. We usually determine face marks from history of emotional elements and actional elements. We propose a method of learning face marks for a natural language dialogue system from chat dialogue data in the Internet, etc. We use a back propagation error learning of a three-layer neural network to learn a model of face marks. In this neural network, the input neurons express emotional parameters and actional categories of texts, and the output neurons express parts of face marks: mouth, eyes, arms, and optional things. The experimental results showed that the learning error was 0.19, and we could get the performance approximately 93% permissible value for the learning set of dialogues and approximately 60% for the evaluation set of dialogues. It also showed that our system acquired the good information of relationship between parts of face marks and emotional and actional elements.