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Knowledge-driven dialogue generation method and device

A knowledge-driven, knowledge-graph technology, applied in the field of knowledge-driven dialogue generation methods and devices, can solve problems such as knowledge graph integration that is no longer applicable

Pending Publication Date: 2021-07-13
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these research works use pre-trained knowledge embedding representations, word vector representations or adjacency matrices to model entities and relationships in knowledge graphs, which makes them no longer suitable for large-scale knowledge graphs. Incorporate into dialogue generation

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  • Knowledge-driven dialogue generation method and device
  • Knowledge-driven dialogue generation method and device

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Embodiment Construction

[0044] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0045] figure 1 It is a schematic flow chart of the speech recognition method provided by the present invention, such as figure 1 shown, including:

[0046] Step S1, inputting the text sequence to be replied into the trained deep neural network dialogue model to obtain the reply text sequence of the text to be replied;

[0047] Wherein, the deep neural network dialogue model stores a featu...

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Abstract

The invention provides a knowledge-driven dialogue generation method and device, and the method comprises the steps: inputting a to-be-replied text sequence into a trained deep neural network dialogue model, and obtaining a reply text sequence of a to-be-replied text, wherein the deep neural network dialogue model stores a feature representation data set of a knowledge graph entity relationship, and the trained deep neural network dialogue model is obtained by training a to-be-replied text sample sequence carrying a real reply text sequence tag and the feature representation data set of the knowledge graph entity relationship. According to the method provided by the invention, the entity representation in the knowledge graph is calculated based on the trained deep neural network dialogue model according to the unstructured information in the text to be replied and the structured information of the knowledge graph, so the knowledge graph can be more efficiently applied to generate dialogue replies with proper semantics and rich information.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a knowledge-driven dialog generation method and device. Background technique [0002] With the rapid development of society, we have now entered the era of information explosion, and people have more urgent pursuit of efficient information processing methods. Among them, the dialogue system has attracted more and more attention from researchers, manufacturers and users, and Siri , Google Assistant, Xiao Ai and other dialogue systems. In the research of dialogue systems, it has always been crucial to construct a successful anthropomorphic dialogue system to generate dialogue responses with appropriate semantics and rich information. [0003] Since the knowledge graph describes the relationship between entities and entities in the world, the amount of knowledge contained is huge. In order to generate dialogue replies with appropriate semantics and rich information, some...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F40/284G06F40/35G06N3/04G06N3/08
CPCG06F16/367G06F40/35G06F40/284G06N3/08G06N3/045
Inventor 黄民烈周昊朱小燕
Owner TSINGHUA UNIV
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