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Model training method and device, electronic equipment and storage medium

A model training and model technology, applied in neural learning methods, biological neural network models, semantic analysis, etc., can solve problems such as complex model training methods, achieve the effect of reducing complexity and building loss functions simply and effectively

Active Publication Date: 2022-06-03
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as the model becomes more complex, so does the way the model is trained

Method used

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  • Model training method and device, electronic equipment and storage medium
  • Model training method and device, electronic equipment and storage medium
  • Model training method and device, electronic equipment and storage medium

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Embodiment Construction

[0030] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

[0031] Neural networks are of high quality in natural language text translation. Take the NMT model as an example, which uses neural network-based techniques to achieve more context-accurate translations instead of translating broken sentences one word at a time. Using a large artificial neural network to calculate the probability of word sequences, NMT puts the co...

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Abstract

The invention provides a model training method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of natural language processing. According to the specific implementation scheme, multiple groups of sentence pairs are obtained, wherein each group of sentence pairs comprises a source language sentence and a target language sentence; for each group of sentence pairs, determining a first word vector of a first semantic element in a source language sentence contained in the sentence pairs, and determining a second word vector of a second semantic element in a target language sentence contained in the sentence pairs; determining a sample pair corresponding to the sentence pair by using the first word vector of the first semantic element and the second word vector of the second semantic element; and determining a first loss function by utilizing the sentence pair and the corresponding sample pair, and training the model by adopting the first loss function. The complexity of the model training process can be reduced.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, and in particular, to the technical field of natural language processing. Background technique [0002] Natural Language Processing (NLP) is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that can realize effective communication between humans and computers using natural language. [0003] Machine Translation (MT, Machine Translation) is the process of translating one natural language (which can be called source language) text into another natural language (which can be called target language) text with the help of the power of machines, and is an important research field of NLP. , is also one of the commonly used services on the Internet. In recent years, neural network models have made significant progress in machine translation tasks and surpassed statistical machine translation, especially...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/58G06F40/30G06N3/04G06N3/08
CPCG06F40/58G06F40/30G06N3/084G06N3/047G06N3/045
Inventor 高鹏至何中军李芝吴华
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD