Dialogue auxiliary system based on team learning and hierarchical reasoning

Abstract

The invention discloses a dialogue auxiliary system for team learning and hierarchical reasoning. The method is characterized in that firstly a generic industry knowledge map which comprises the universal entities and attribute entries outside the industry is crawled and generated, thereby conveniently meeting the requirement of a user for universal knowledge query, and facilitating the realization of the rapid migration of knowledge bases among the industries; secondly, a multi-level reasoning network provided by the invention can realize the complex semantic reasoning capability, can realizethe man-machine conversation based on a reasoning process, and can carry out the accurate service recommendation and product marketing through the reasoning network at the same time; and finally, thestrategy learning network based on reinforcement learning can learn the sorting strategy by utilizing the accumulated historical interaction experience, so that the user experience is continuously improved. The functional extension and effect upgrading of an original dialogue system can be realized by deploying the system, and the user experience is improved.

Description

technical field [0001] The invention relates to a dialogue assistance system based on team learning and hierarchical reasoning, which belongs to the field of human-computer interaction in artificial intelligence. Background technique [0002] The traditional dialogue system is mainly based on knowledge engineering to build a knowledge tree. During the dialogue process, the method of searching and matching the knowledge tree is used to obtain the answers to the edited questions. The multi-round interaction process is mainly based on the matching of some words and entity slot values. This kind of dialogue system can meet the needs of self-service voice interaction in vertical industries to a certain extent, but there are still several problems: First, the common-sense question-and-answer ability of this kind of dialogue system is relatively weak, because the knowledge tree is manually edited and established Yes, so the questions that the robot can answer are limited to the ran...

AI Technical Summary

Problems solved by technology

This kind of dialogue system can meet the needs of self-service voice interaction in vertical industries to a certain extent, but there are still several problems: First, the common-sense question-and-answer ability of this kind of dialogue system is relatively weak, because the knowledge tree is manually edited and established Yes, so the questions that the robot can answer are limited to the range of manual presets; second, in daily business handling, customers sometimes raise some dialogues that require reasoning, such as recommending financial products based on risk preferences, and choosing products based on users’ emotions. Correspondingly speaking, the traditional dialogue system cannot carry out this kind of inferential dialogue; the third point is that it cannot adaptively learn the sorting strategy and dialogue of multiple-choice answers based on parameters such as the user's emotional feedback, the number of interaction rounds, and service satisfaction. strategy; the fourth point, a single machine has fewer dialogue logs and cannot provide effective learning samples

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