TRANSLATION METHOD, CLASSIFICATION MODEL TRAINING METHOD, DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20230015313A1

    公开(公告)日:2023-01-19

    申请号:US17656160

    申请日:2022-03-23

    Abstract: Disclosed are a translation method, a classification model training method, a device and a storage medium, which relate to the field of computer technologies, particularly to the field of artificial intelligence such as natural language processing and deep learning. The translation method includes: obtaining a current processing unit of a source language text based on a segmented word in the source language text; determining a classification result of the current processing unit with a classification model; and in response to determining that the classification result is the current processing unit being translatable separately, translating the current processing unit to obtain translation result in a target language corresponding to the current processing unit.

    METHOD AND DEVICE FOR TRAINING SPEECH TRANSLATION MODEL, AND STORAGE MEDIUM

    公开(公告)号:US20250054494A1

    公开(公告)日:2025-02-13

    申请号:US18930081

    申请日:2024-10-29

    Abstract: A method for training a speech translation model includes: obtaining a trained first text translation model and a speech recognition model, and constructing a candidate speech translation model to be trained based on the first text translation model and the speech recognition model; obtaining at least one of a first sample source language speech or a first sample source language text to obtain a training sample of the candidate speech translation model; and training the candidate speech translation model based on the training sample until the training is completed, and obtaining a trained target speech translation model.

    Translation method, apparatus and storage medium

    公开(公告)号:US12210956B2

    公开(公告)日:2025-01-28

    申请号:US18074853

    申请日:2022-12-05

    Abstract: The present disclosure provides a translation method and apparatus, an electronic device, and a non-transitory storage medium. An implementation includes: determining an encoded feature of a sentence to be translated by an encoding module; determining, by a graph network module, a knowledge fusion feature of the sentence to be translated based on a preset graph network, wherein the preset graph network is constructed based on a polysemous word in a source language corresponding to the sentence to be translated and a plurality of translated words corresponding to the polysemous word in a target language; determining, by a decoding network, a translated sentence corresponding to the sentence to be translated based on the encoded feature and the knowledge fusion feature.

    Translation method, electronic device and storage medium

    公开(公告)号:US12197882B2

    公开(公告)日:2025-01-14

    申请号:US17885152

    申请日:2022-08-10

    Abstract: A translation method, an electronic device and a storage medium, which relate to the field of artificial intelligence technologies, such as machine learning technologies, information processing technologies, are disclosed. An implementation includes: acquiring an intermediate translation result generated by each of multiple pre-trained translation models for a to-be-translated specified sentence in a same iteration of a translation process, so as to obtain multiple intermediate translation results; acquiring a co-occurrence word based on the multiple intermediate translation results; and acquiring a target translation result of the specified sentence based on the co-occurrence word.

    TRANSLATION MODEL TRAINING METHOD, TRANSLATION METHOD, APPARATUS, DEVICE, AND STORAGE MEDIUM

    公开(公告)号:US20230267286A1

    公开(公告)日:2023-08-24

    申请号:US17879965

    申请日:2022-08-03

    CPC classification number: G06F40/58 G06F40/211

    Abstract: Provided are a translation model training method, a translation method, a device, and a storage medium, and relates to a field of computer technology, and in particular, to artificial intelligence fields such as natural language processing, machine translation and the like. The translation model training method includes: processing a sample document, to obtain an RST discourse structure tree in a dependency form of the sample document, a side in the RST discourse structure tree in the dependency form indicating an RST relationship in a discourse of the sample document; determining an attention mechanism of a translation model to be trained, based on the RST relationship in the RST discourse structure tree in the dependency form; and inputting the RST discourse structure tree in the dependency form and the sample document into the translation model to be trained for training, to obtain a trained translation model.

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