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Man-machine multi-round dialogue method oriented to travel field

A human-machine and domain technology, applied in the field of human-machine dialogue, can solve the problem of difficulty in extracting intention information and slot information, and achieve the effect of overcoming limitations

Active Publication Date: 2018-12-21
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the current multi-round dialogue system has difficulty in extracting the intent information and slot information of user questions

Method used

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  • Man-machine multi-round dialogue method oriented to travel field
  • Man-machine multi-round dialogue method oriented to travel field
  • Man-machine multi-round dialogue method oriented to travel field

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Experimental program
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specific Embodiment approach 1

[0021] Specific implementation mode one: combine figure 1 This embodiment will be described. A human-machine multi-round dialogue method oriented to the field of travel described in this embodiment, the specific steps of the method are:

[0022] Step 1. Standardize the user's current question, and then, if there are clear demonstrative pronouns or lack of sentence substructure in the current question, according to the slot information involved in the previous interaction with the user, the instructions in the current question Pronouns and missing sentence substructures are replaced or filled in; current questions are processed;

[0023] Step 2. Use DAN, CNN or BLSTM model to obtain the intention information of the current question after step 1 processing: input the current question after step 1 processing into DAN, CNN or BLSTM model; the output of DAN, CNN or BLSTM model is passed through softmax The operation obtains the intention probability of the current question, and u...

specific Embodiment approach 2

[0027] Specific implementation mode two: combination figure 2 This embodiment will be described. This embodiment further defines the human-machine multi-round dialogue method for the travel field described in the first embodiment; the specific process of using the DAN model to obtain the intention information of the current question after step one is as follows:

[0028] Before using deep learning to conduct experiments, the present invention first uses the word2vec open-sourced by Google to train word vectors. The data used comes from the Chinese web page text data of Wikipedia in April 2018. The CBOW model is adopted, and the word vector dimension is 300 dimensions. Subsequent deep learning models are based on the word vectors completed by the training;

[0029] Manual construction of question data R in the field of travel 1 Article, using a custom query to crawl the question data R in the travel field 2 Article, to obtain the question data R of the chat class and the ve...

specific Embodiment approach 3

[0038] Specific implementation mode three: combination image 3 Describe this embodiment. This embodiment further defines a human-machine multi-round dialogue method for the travel field described in Embodiment 1; the CNN model is used to obtain the intention information of the current question after step 1 processing. The specific process is:

[0039] Manual construction of question data R in the field of travel 1 Article, using a custom query to crawl the question data R in the travel field 2 Article, to obtain the question data R of the chat class and the vertical class in the SMP2017 evaluation task 3 strip; put all R 1 +R 2 +R 3 The questions are randomly sorted, and after random sorting, a part of the questions are randomly selected as the training set, and the rest of the questions are used as the test set; the CNN model includes an input layer, a convolutional layer, a pooling layer and a fully connected layer;

[0040] The training process of the CNN model is as...

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Abstract

The invention relates to a man-machine multi-round dialogue method oriented to the travel field, which is used in the information technology field. The invention solves the problem that the current multi-round conversation system has difficulty in extracting the intention information and the slot information of the user question. The invention normalizes the short text question, extracts the intention information of the normalized short text question by using DAN, CNN or BLSTM model, and obtains Micro. F1 value was 93.47%. Using the BLSTM-CRF model to extract the slot information of the normalized short text questions, and the model achieves the ideal effect with F1 value of 89.47%. Taking the historical slot information and the slot information of the current question as inputs, the current conversation state information is determined, and the next reply strategy is determined according to the intention information of the current question. According to the determined reply strategy, the corresponding template is selected to reply to the user. The invention can be applied to the field of information technology.

Description

technical field [0001] The invention belongs to the technical field of man-machine dialogue, and in particular relates to a man-machine multi-round dialogue method oriented to the travel field. Background technique [0002] The human-computer dialogue system is a human-computer two-way information exchange system that regards the machine as a cognitive subject, and is a way to realize human-computer interaction. In the dialogue system, users come with a clear purpose, hoping to obtain information or services that meet specific constraints, such as ordering tickets, ordering meals, looking for products, etc. User needs often need to be stated in multiple rounds, and users can improve their needs during the dialogue process. [0003] At present, research on multi-round dialogue systems in the field of travel at home and abroad mainly includes its pipeline (divided into three parts: natural language understanding, dialogue management, and natural language generation) and the u...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27G06K9/62
CPCG06F40/211G06F40/289G06F40/295G06F18/2414G06F18/214G06F40/35G06N3/08G06N7/01G06N3/044G06N3/045
Inventor 赵铁军郑德权林先辉曹海龙朱聪慧徐冰杨沐昀
Owner HARBIN INST OF TECH
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