Multi-wheel dialogue management method for hierarchical attention LSTM and knowledge graph

Abstract

The invention discloses a multi-turn dialog management method for hierarchical attention LSTM and knowledge graph, and belongs to the field of natural language processing. The method has the core idea: taking conversation contents of the user and the system in the conversation as a context, extracting the context deep semantic through important and timing information of the word and sentence level, specifically in two steps, firstly extracting sentence semantics at the word level by utilizing the first attention mechanism LSTM, and then extracting context semantics through the second attentionmechanism LSTM at the sentence level; the attention mechanism keeps important information, and the attention mechanism is realized through the knowledge graph as external knowledge, the LSTM retainstiming information that collectively identifies the user intent and the recognition result is used to determine whether to open the next session. According to the multi-turn dialog management method for hierarchical attention LSTM and knowledge graph, the knowledge graph and the LSTM are utilized to learn the contextual deep semantics, the attention mechanism is utilized to filter out useless information, and therefore the user intention identification efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to a multi-round dialogue management method of hierarchical attention LSTM and knowledge graph, belonging to the field of natural language processing. Background technique [0002] With the development of computing technology and artificial intelligence technology, dialogue management systems have been more and more extensively studied, especially task-oriented dialogue management systems, which can be used in customer services such as air ticket reservations to help companies effectively reduce operating costs , has important application value. According to the different degrees of intelligence of the dialogue system, the dialogue forms of the intelligent dialogue system can be simply divided into two types: single-turn dialogue and multi-turn dialogue. A single round of dialogue is relatively simple and has no memory function, while there is a connection between questions and answers in multi-round dialogues. Intent recognition...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the technical defects of lack of context information and external knowledge in the user intention judgment existing in the existing multi-round dialogue management method, and propose a multi-round dialogue management method based on hierarchical attention LSTM and knowledge graph

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