Context-based dialogue generation method and system

A technology for generating systems and contexts, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as lack of large corpora, and achieve the effect of being conducive to training, reducing interference, and reflecting robustness

Active Publication Date: 2021-02-05
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, chatbots' methods in task-based dialogue are reaching maturity

Method used

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  • Context-based dialogue generation method and system
  • Context-based dialogue generation method and system
  • Context-based dialogue generation method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] This embodiment provides a context-based dialogue generation method;

[0033] Context-based dialogue generation methods, including:

[0034] S101: Obtain the current dialogue sentence, the previous sentence of the current dialogue and the following sentence of the current dialogue;

[0035] S102: Input the current dialogue sentence, the previous sentence of the current dialogue and the following sentence of the current dialogue into the pre-trained codec, and the codec outputs the predicted dialogue sentence of the current dialogue sentence.

[0036] As one or more embodiments, S102: Input the current dialogue sentence, the previous sentence of the current dialogue and the following sentence of the current dialogue into the pre-trained codec, and the codec outputs the predicted dialogue sentence of the current dialogue sentence ; Specific steps include:

[0037] Input the current dialogue sentence, the previous sentence of the current dialogue and the following senten...

Embodiment 2

[0095] This embodiment provides a context-based dialogue generation system;

[0096] Context-based dialogue generation systems, including:

[0097] An acquisition module configured to: acquire the current dialogue statement, the previous statement of the current dialogue and the following statement of the current dialogue;

[0098] The prediction module is configured to: input the current dialogue sentence, the previous sentence of the current dialogue and the following sentence of the current dialogue into the pre-trained codec, and the codec outputs the predicted dialogue sentence of the current dialogue sentence.

[0099] It should be noted here that the above acquisition module and prediction module correspond to steps S101 to S102 in the first embodiment, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to those disclosed in the first embodiment above content. It should be noted tha...

Embodiment 3

[0103] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0104] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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PUM

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Abstract

The invention discloses a context-based dialogue generation method and system. The method comprises the steps of obtaining a current dialogue statement, a previous statement of a current dialogue anda next statement of the current dialogue; and inputting the current dialogue statement, the previous statement of the current dialogue and the next statement of the current dialogue into a pre-trainedcodec, and outputting a predicted dialogue statement of the current dialogue statement by the codec. According to the method, the method can play a role in linking the previous part and the next part; the contextual information related to the current dialogue can be combined to generate more effective information, on one hand, the dialogue can be smoother, and the consistency and logicality of the dialogue before and after the dialogue are kept; on the other hand, more premise input information can be provided for the neural network, and research shows that the rich input information is beneficial for the neural network to generate more meaningful answers.

Description

technical field [0001] The present application relates to the technical field of dialog generation, in particular to a context-based dialog generation method and system. Background technique [0002] The statements in this section merely mention the background art related to this application, and do not necessarily constitute the prior art. [0003] The characteristics of low investment and high return unique to online virtual contacts make online operations more and more popular. At present, online dialogue, online office, online shopping, and online diagnosis and treatment have become a large part of people's lives. In the context of network development, chatbots are gradually moving towards the center of people’s vision, mainly due to the fact that chatbots are everywhere in people’s online life: when shopping online, robots can replace customer service to recommend products that meet customer needs; The attending doctor can be recommended to the patient according to th...

Claims

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Application Information

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IPC IPC(8): G06F16/332G06F40/211G06N3/04G06N3/08G06K9/62
CPCG06F16/3329G06F40/211G06N3/08G06N3/045G06F18/253
Inventor 解福刘悦刘凤鸣于凤洋徐传杰
Owner SHANDONG NORMAL UNIV
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