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METHOD AND APPARATUS FOR TRAINING MODELS IN MACHINE TRANSLATION, ELECTRONIC DEVICE AND STORAGE MEDIUM

机译:用于机器翻译,电子设备和存储介质的培训模型的方法和装置

摘要

A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second sample training set. With the above-mentioned technical solution of the present application, by training the two models jointly, while the semantic similarity model is trained, the machine translation model may be optimized and nurtures the semantic similarity model, thus further improving the accuracy of the semantic similarity model.
机译:公开了一种用于训练机器翻译,电子设备和存储介质的模型的方法和装置,其涉及自然语言处理技术领域和深度学习技术领域。一种实现包括使用机器翻译模型和语义相似性模型的并行语料库和创建第一训练样本集的并行语料库,包括挖掘一组样本的类似目标句子;用第一个训练样本集培训机器翻译模型;使用机器翻译模型和语义相似性模型,基于并行语料库挖掘各种样品中的每个样本的负样本,并创建第二次训练样本集;并使用第二个样本培训集培训语义相似性模型。通过上述本申请的技术解决方案,通过共同训练两个模型,而在培训语义相似性模型的同时,机器翻译模型可以优化并培养语义相似性模型,从而进一步提高语义相似度的准确性模型。

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