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Event corpus purification method based on multi-agent reinforcement learning

A multi-agent, enhanced learning technology, applied in machine learning, database models, relational databases, etc., can solve problems such as label noise and adverse effects of joint extraction models

Pending Publication Date: 2021-09-10
CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This leads to the problem of label noise in the large amount of labeled data sets generated by distant supervision methods, and these noises will adversely affect the joint extraction model.

Method used

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  • Event corpus purification method based on multi-agent reinforcement learning
  • Event corpus purification method based on multi-agent reinforcement learning
  • Event corpus purification method based on multi-agent reinforcement learning

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Embodiment Construction

[0038] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0039] Such as figure 1 As shown, the present invention provides a method for purifying event corpus based on multi-agent reinforcement learning, including

[0040] S1. Before starting model training, it is necessary to initialize and reset the environment and agents, and set corresponding training parameters;

[0041] S2. By performing corresponding purification and optimization actions in the environment, the agent forms a serie...

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Abstract

The invention relates to an event corpus purification method based on multi-agent reinforcement learning. The method comprises the following steps: carrying out initialization reset on an environment and agents before model training, and setting corresponding training parameters; enabling the intelligent agent to perform corresponding purification and optimization actions in the environment to form a series of data required by training, sampling the data, and storing in a data cache region for subsequent training; when the data quantity in the data cache region reaches a preset value, starting to use the data to train and update the real networks of all agents; after the real network is updated, updating target networks of all agents through an irregular parameter copying method; and repeating the above steps until the training frequency reaches a preset training frequency. The labeled data is purified and optimized, so that the problem of data label noise encountered in the training process of the sequence labeling joint extraction model is solved, and the effect of the event entity relation joint extraction task is improved.

Description

technical field [0001] The invention relates to the field of multi-agent reinforcement learning methods, in particular to a method for purifying an event corpus based on multi-agent reinforcement learning. Background technique [0002] Reinforcement learning (MARL) is a method of machine learning. According to the number of agents, it can be divided into single-agent reinforcement learning and multi-agent reinforcement learning. Among them, multi-agent reinforcement learning has a wider range of application scenarios and is the solution A key tool for many real-world problems. In multi-agent reinforcement learning, according to the different task relations of agents, it can be divided into: fully cooperative tasks, fully competitive tasks and mixed tasks. Here we only consider fully cooperative tasks. [0003] In the multi-agent reinforcement learning training under fully cooperative tasks, the agent aims at maximizing the joint reward, selects an action according to its ow...

Claims

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Application Information

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IPC IPC(8): G06F16/28G06N20/00
CPCG06F16/288G06N20/00
Inventor 后敬甲王悦白璐崔丽欣
Owner CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS
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