Classification model training method and device, translation method and device, and electronic equipment

A classification model and training method technology, applied in machine learning, natural language processing, and computer fields, can solve problems such as inaccurate translation and low translation quality, and achieve the effect of high quality, accurate results, and low delay

Pending Publication Date: 2022-02-08
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Problems solved by technology

However, the machine simultaneous interpretation in the prior art ofte

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  • Classification model training method and device, translation method and device, and electronic equipment
  • Classification model training method and device, translation method and device, and electronic equipment
  • Classification model training method and device, translation method and device, and electronic equipment

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Embodiment Construction

[0039] Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded 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 disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0040] The embodiment of the present disclosure provides a classification model training method, figure 1 It is a flowchart of a classification model training method according to an embodiment of the present disclosure. This method can be applied to a classification model training device. For example, when the device is deployed on a terminal or a server or other proces...

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Abstract

The invention provides a classification model training method and device, a translation method and device and electronic equipment, and relates to the technical field of natural language processing and machine learning. The method comprises the following steps: acquiring bilingual parallel corpora, wherein the bilingual parallel corpora comprise a source language corpus and a target language corpus; determining translable fragments and untranslable fragments in the source language corpus, wherein the translable fragments are determined based on unambiguous fragments in the source language corpus; and training a classification model based on the translatable fragments and the untranslatable fragments. According to the above technical scheme, the classification model is trained based on the translable fragments and the untranslable fragments, the trained classification model is applied to translation, a translation result can be more accurate, and requirements for high quality and low time delay are met.

Description

technical field [0001] The present disclosure relates to the technical field of computers, and in particular to the technical fields of natural language processing and machine learning. Background technique [0002] Simultaneous interpretation is the process of converting one natural language (source language) into another natural language (target language) in real time. At present, simultaneous interpretation is mainly done by artificial parliamentarians, but there are only 3,000 qualified simultaneous interpreters in the world, and they can only work continuously for 15-20 minutes, and the translation rate is only 60%. Therefore, it is hoped to use computers to assist humans in simultaneous interpretation, that is, to use machines for speech translation, also known as machine simultaneous interpretation. However, the machine simultaneous interpretation in the prior art often has inaccurate translations, and the translation quality is not high. Contents of the invention ...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F40/58G06F40/211G06F40/289G06F40/30
CPCG06F16/35G06F40/58G06F40/211G06F40/289G06F40/30
Inventor 张睿卿张传强何中军吴华
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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