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A man-machine conversation method based on actor-critic reinforcement learning algorithm in cyclic network

A technology of reinforcement learning and human-computer dialogue, applied in neural learning methods, biological neural network models, computing, etc., can solve problems such as repetition and increased operating costs

Active Publication Date: 2019-02-01
POLIXIR TECH LTD
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Problems solved by technology

For shopkeepers, consultation and after-sales information from customers are mostly repetitive
In addition, for some larger shops, shop owners often need to hire customer service, which will undoubtedly increase operating costs

Method used

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  • A man-machine conversation method based on actor-critic reinforcement learning algorithm in cyclic network
  • A man-machine conversation method based on actor-critic reinforcement learning algorithm in cyclic network
  • A man-machine conversation method based on actor-critic reinforcement learning algorithm in cyclic network

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Embodiment Construction

[0074] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0075] refer to Figure 1 to Figure 4 :

[0076] S1: Supervised model training. We use an open source dataset to conduct supervised training on the Gated Recurrent Unit Network to obtain a better dialogue generation model.

[0077] S2: Asynchronous model training. Based on the gated recurrent unit network model obtained from S1, we built two networks, which we call the “actor” network and the “critic” network, respectively. We distribute this pair of models to multiple processes and let them continuously generate new dialogues. We further tune network parameters based on the dialogues they gen...

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Abstract

The invention relates to a circulating network man-machine conversation method based on an actor critic reinforcement learning algorithm. The system consists of two subsystems: dialogue generation system and emotion analysis system. The session generation system is based on the gate loop unit network model and uses the tagged session data set for training. Furthermore, we optimize the parameters of the trained model using the actor-critic algorithm in reinforcement learning. That is, we use the trained model to build two networks, called the 'actor' network and the 'critic' network; Further, in order to reduce training time and improve resource utilization, we have created multiple processes that assign a pair of 'actors' and 'critics' to each process.

Description

technical field [0001] The invention relates to a man-machine dialogue method, in particular to a recurrent network man-machine dialogue method based on actor-critic reinforcement learning algorithm. Background technique [0002] With the development of science and technology, new achievements are constantly emerging in the field of natural language processing, among which the development of human-computer dialogue technology is particularly eye-catching. At present, such as in the field of e-commerce, people communicate with each other mainly through online chatting. For store owners, most of the inquiries and after-sales information from customers are repetitive. In addition, for some larger shops, shop owners often need to hire customer service, which will undoubtedly increase operating costs. For customers, they hope that the shopkeeper can reply as soon as possible and decently. The human-computer dialogue system can complete the work of customer service, reduce the ...

Claims

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Application Information

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IPC IPC(8): G06F16/332G06F16/33G06N3/04G06N3/08
CPCG06N3/084G06N3/047G06N3/045
Inventor 王艺深章宗长陈浩然
Owner POLIXIR TECH LTD
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