Machine self-learning construction knowledge atlas training method based on neural network

Abstract

The invention discloses a machine self-learning construction knowledge atlas training method based on a neural network. The method comprises the following steps of acquiring a statement based on a natural situation which is sent by a user, using a voice noise reduction algorithm to carry out filtering noise reduction on an input statement, and determining a matched feedback statement; if the matched feedback statement does not exist, according to a neural network dialogue model, giving an answer of the statement sent by the user, which includes the following steps of constructing a coding layer of a user sending statement model to be a first neural network; analyzing a user sending statement in the first neural network and acquiring a first intermediate vector used for expressing a user sending statement meaning; and constructing a decoding layer of a dialogue generation model to be a second neural network, analyzing the intermediate vector in the second neural network, and acquiring a vector group used for expressing a statement answer. In the invention, a threshold voice noise reduction algorithm is used to acquire a small mean square error and a signal to noise ratio of a reconstruction voice signal is increased.

Description

technical field [0001] The invention relates to the field of intelligent robots, in particular to a neural network-based machine self-learning method for constructing a knowledge map training method. Background technique [0002] Chatterbot (chatterbot) is a program used to simulate human dialogue or chat. The reason for chatbots is that the developers put the answers they are interested in into the database. When a question is thrown to the chatbot, it uses the similarity matching algorithm to find the most similar question from the database, and then based on the question and The corresponding relationship of the answer, give the most appropriate answer, and reply to its chat partner. [0003] However, in the current robot chat scenario, when the same or similar question matching the user's requested question cannot be found in the robot knowledge base, the robot cannot return the correct or appropriate answer to the user. [0004] In addition to the problem of limited k...

AI Technical Summary

Problems solved by technology

[0003] However, in the current robot chat scenario, when the same or similar questions that match the user's requested question cannot be found in the robot knowledge base, the robot cannot return the correct or appropriate answer to the user.

[0004] In addition to the limited knowledge base of the robot, the limitation of the existing technology will also lead to errors in semantic understanding, thus making the user experience in the communication process with the robot poor.

In addition, the knowledge reasoning process of existing technologies also has certain limitations in knowledge reasoning. Traditional knowledge reasoning relies on program developers to write some rules to solve knowledge reasoning problems.

However, it is unimaginable for developers to exhaustively enumerate and formulate these rules

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