A cross-modal bilateral personalized human-computer social dialogue generation method and system

Abstract

The invention discloses a method and system for generating a cross-modal bilateral personalized human-machine social dialogue. The invention includes encoding the dialogue context, encoding the personalized information of the robot, encoding the personalized information of the user, and encoding the output result at the last moment Perform weighted fusion to obtain a weighted fusion code, and then input the code of the output result at the previous moment into the decoder of the bilateral personalized generation model to generate the best N candidate reply list, and select the candidate reply with the largest conditional mutual information abundance value as the final output. The present invention fuses the personalized information in a cross-modal way, and at the same time considers the personalized information of the characters on both sides of the interaction, and makes full use of the personalization of the two sides of the interaction on the premise of ensuring that the reply content is reasonable, the grammar is smooth, and the logic is coherent. features that generate personalized, personalized responses.

Description

technical field [0001] The invention relates to the technical field based on human-computer interaction, in particular to a method and system for generating a cross-modal bilateral personalized human-computer social dialogue. Background technique [0002] With the advancement of science and technology, human-computer interaction is gradually developing toward intelligence and personalization, and the interaction between humans and robots is getting closer to the interaction between humans in the real world. The traditional human-machine social dialogue generation belongs to the field of natural language processing, which mainly studies the ability of robots to make natural responses according to the user's text input. Different from interpersonal communication, vision is the main sensory source for people to receive external information, and people can make natural and personalized expressions based on external information. Therefore, in order to make the robot more "human-...

AI Technical Summary

Problems solved by technology

[0005] However, in real social interactions between people, both parties can learn each other's personalized information. When replying, the respondent should not only focus on his own personalized expression, but also consider the other party's personalized characteristics and the other party's information. Reply to the questions, ignore the human-computer interaction of the user's personalized information, which will make people feel disgusted and disgusted, and reduce the user experience

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