Translation model training method, statement translation method and device and storage medium

Abstract

The invention discloses a translation model training method comprising the steps pf acquiring a training sample set wherein the training sample set comprises a plurality of training samples; determining a disturbance sample set corresponding to each training sample in the training sample set, with the disturbance sample set comprising at least one disturbance sample, the semantic similarity between the disturbance sample and the corresponding training sample being higher than a first preset value; and training the initial translation model by using the plurality of training samples and the disturbance sample set corresponding to each training sample to obtain a target translation model. According to the scheme provided by the embodiment of the invention, a disturbance sample is introducedinto model training, so that the robustness and translation quality of machine translation can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a translation model training method, a sentence translation method, computer equipment, terminal equipment, and a computer-readable storage medium. Background technique [0002] With the development of artificial intelligence, machine translation has been widely used, such as simultaneous interpretation and chat content translation, which are based on machine translation to convert one input language into another language output. [0003] Neural machine translation is a machine translation model based entirely on neural networks. It has achieved a good level of translation in many language pairs and has been widely used in various machine translation products. However, since the neural machine translation model is based on a complete neural network, the global nature of its modeling causes each output of the target end to depend on each word input by the source en...

AI Technical Summary

Problems solved by technology

However, since the neural machine translation model is based on a complete neural network, the global nature of its modeling causes each output of the target end to depend on each word input by the source end, making it overly sensitive to small disturbances in the input

For example, in the translation from Chinese to English, the user inputs "They are not afraid of difficulties to make Go AI", the English translation given by the machine translation model is "They are not afraid of difficulties to make Go AI", however, when the user enters a similar The sentence "They are not afraid of difficulties to make Go AI", the output of machine translation has changed drastically, and the result is "They are not afraid to make Go AI", although the user just replaced one of the words with a synonym, the translation result drastic changes have taken place

[0004] It can be seen that the stability of the current neural machine translation, that is, the robustness is relatively poor

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