Training method and device for sequence labeling model

A technology of sequence labeling and training methods, which is applied in the fields of instruments, electrical digital data processing, calculation, etc., and can solve problems such as inability to obtain sequence labeling models and insufficient sample data

Pending Publication Date: 2020-10-02
WEBANK (CHINA)
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Problems solved by technology

[0004] The present invention provides a training method and device for a sequence labeling model, which is used to solve the problem in t

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  • Training method and device for sequence labeling model
  • Training method and device for sequence labeling model
  • Training method and device for sequence labeling model

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Embodiment Construction

[0086] In order to effectively train the sequence labeling model in the case of insufficient sample data, in the embodiment of the present invention, the sequence labeling model to be trained and the set of sample training sentences are obtained, and then, based on the set of sample training sentences, the sequence is The labeling model is trained to obtain the first loss information, and then the adversarial disturbance factor is determined according to the model parameters of the sequence labeling model, and the sequence labeling model is trained based on the set of sample training sentences added with the adversarial disturbance factor, to obtain second loss information, and then, calculate target loss information based on the first loss information and the second loss information, and adjust model parameters of the sequence labeling model based on the target loss information and perform iterative training, And when it is determined that the preset convergence condition is m...

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Abstract

The invention relates to the field of natural language processing, and particularly relates to a training method and device of a sequence labeling model. The method is used for effectively training asequence labeling model under the condition of insufficient sample data volume. The method comprises the following steps of: training a sequence labeling model based on the sample training statement set; obtaining first loss information, determining an adversarial disturbance factor according to the model parameters; and obtaining second loss information based on the sample training statement setadded with the adversarial disturbance factor, adjusting model parameters of the sequence labeling model based on target loss information obtained through calculation of the first loss information andthe second loss information, performing iterative training, and determining that a convergence condition is satisfied. Thus, by adding the anti-disturbance factor, different loss information can be obtained based on one sample training statement, the generalization ability of the sequence labeling model obtained through training is higher, the precision is higher, introduction of unnecessary noise interference is avoided, and resource consumption is reduced.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a training method and device for a sequence labeling model. Background technique [0002] The sequence labeling problem is an important and widely used problem in the field of natural language processing. After people complete the training of the sequence labeling model based on the training samples, they can use the trained sequence labeling model to realize the input sentences. Sequence labeling. However, when training sequence labeling models, there are insufficient samples in many scenarios. [0003] In the prior art, in order to obtain a sufficient sample size, data enhancement processing is usually used to obtain multiple sample data from one sample data, and then sequence labeling model training is performed through the obtained multiple sample data. However, when the sample data generated by data enhancement processing is used for training, the noise caused by...

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Application Information

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IPC IPC(8): G06F40/117G06F40/205
CPCG06F40/117G06F40/205
Inventor 周楠楠杨海军徐倩
Owner WEBANK (CHINA)
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