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Question answering model training method and device

A training method and model technology, applied in the field of artificial intelligence, can solve the problems such as the speed of reply needs to be improved and the accuracy of the user's reply content is not high, so as to improve the processing capacity, complete the question and answer processing tasks accurately and efficiently, and ensure the prediction accuracy.

Active Publication Date: 2021-11-16
BEIJING KINGSOFT DIGITAL ENTERTAINMENT CO LTD
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, the current question-and-answer model is not very accurate in replying to users, and the reply speed needs to be improved.

Method used

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  • Question answering model training method and device
  • Question answering model training method and device
  • Question answering model training method and device

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Embodiment Construction

[0057]In the following description, numerous specific details are set forth in order to provide a thorough understanding of the application. However, the present application can be implemented in many other ways different from those described here, and those skilled in the art can make similar promotions without violating the connotation of the present application. Therefore, the present application is not limited by the specific implementation disclosed below.

[0058] Terms used in one or more embodiments of the present application are for the purpose of describing specific embodiments only, and are not intended to limit the one or more embodiments of the present application. As used in one or more embodiments of this application and the appended claims, the singular forms "a", "the", and "the" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the...

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Abstract

The present application provides a question and answer model training method and device, wherein the question and answer model training method includes: constructing an initial text meaning group corresponding to a sample corpus, and generating a scene-oriented vocabulary corresponding to the sample corpus based on the initial text meaning group space; obtain training samples, and determine the sample phrases corresponding to the training samples; query the scene-oriented vocabulary space based on the sample phrases, determine the target text meaning group corresponding to the training samples according to the query results; use the target The text meaning group and the training samples train the initial question answering model until a target question answering model meeting the training stop condition is obtained.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a method and device for training a question answering model. Background technique [0002] With the development of the artificial intelligence industry, the proportion of question-answering models in practical applications has gradually increased, and users have higher and higher requirements for the accuracy and efficiency of responses to question-answering models. In the prior art, the question answering model generally responds by understanding the questions sent by the user and generating targeted answers according to the user's questions. However, the current question-and-answer model is not very accurate in replying to users, and the reply speed needs to be improved. Therefore, how to solve the above problems and improve the accuracy and speed of replies for the question answering model has become an urgent problem to be solved by those skilled in...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/332G06F16/33G06F16/35G06F40/216G06F40/30G06K9/62G06N3/04G06N3/08
CPCG06F16/3329G06F16/3344G06F16/35G06F40/30G06N3/08G06F40/289G06N3/044G06N3/045G06F18/22
Inventor 冯晓阳李长亮姬子明
Owner BEIJING KINGSOFT DIGITAL ENTERTAINMENT CO LTD
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