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

A technology of knowledge graph and dialogue management, applied in the field of natural language processing, which can solve the problems of lack of contextual information and external knowledge, etc.

Active Publication Date: 2018-11-23
北京寻领科技有限公司
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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 exist

Method used

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

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Embodiment 1

[0083] The present embodiment has described the concrete implementation process of the present invention, as figure 1 shown.

[0084] From figure 1 It can be seen that the flow of the multi-round dialogue management method of a hierarchical attention LSTM and knowledge graph in the present invention is as follows:

[0085] Step A constructs a vocabulary; extract all the entities in the knowledge map, and the entity represents the user's intention, then all the words in the vocabulary are the collection of user's intention;

[0086] Step B crawls data; use the scrapy tool to build a crawler framework, for a certain word in the vocabulary in step A, crawl 20 sentences containing the word to meet the stop condition, then the calculation method of the size of the corpus is as follows formula (9):

[0087] Len=num(UI all )*20 (9)

[0088] Among them, Len represents the size of the crawled corpus, num(UI all ) represents the number of all user intents;

[0089] Step C learns w...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F17/30G06N3/04G06N3/08
CPCG06N3/08G06F40/205G06F40/30G06N3/048
Inventor 高扬王丹其他发明人请求不公开姓名
Owner 北京寻领科技有限公司
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