8 results about "Back translation" patented technology

The invention relates to a training method of an unsupervised language translation model and a language translation method and device. The implementation scheme is as follows: performing grammatical analysis and coding processing on unsupervised training corpora to obtain a grammatical feature vector; respectively inputting the training corpora into the corresponding monolingual word embedding layer and the shared word embedding layer to obtain monolingual word embedding and shared word embedding, and generating semantic vectors according to the monolingual word embedding and the shared word embedding; the shared word embedding layer is constructed based on a mixed corpus containing a source language and a target language; performing fusion processing on the grammar feature vector and the semantic vector to obtain a fusion vector of the training corpus; training an unsupervised pre-training model by using the fusion vector; and initializing an encoder of the language translation model by adopting a weight parameter of the trained unsupervised pre-training model, and performing unsupervised training on the language translation model by alternately executing a de-noising automatic encoder and a reverse translation task. According to the invention, the accuracy, fluency and robustness of translation can be improved.

A translation device (1) is configured so as to comprise: an original text acquisition unit (11) that acquires an original text to be translated; a first translation unit (12) that causes the original text acquired by the original text acquisition unit (11) to be translated into a first language; a back translation unit (13) that causes the translated text by the first translation unit (12) to be translated back; a display processing unit (16) that causes a display device (3) to display the translated-back text by the back translation unit (13); and a correction reception unit (14) that receives correction of the original text after the translated-back text is displayed by the display processing unit (16). The translation device (1) also comprises a second translation unit (15) that, when the correction reception unit (14) receives the correction of the original text, causes the corrected original text received by the correction reception unit (14) to be translated into a second language.

The application provides a question generation model training method and device, wherein the question generation model training method comprises the following steps: obtaining a triple in a target knowledge base; creating an initial question template according to the triple, and performing back translation processing on the initial question template to obtain an extended question template; determining a mapping relationship between the triple and the initial question template and the extended question template based on a relationship contained in the triple; constructing a sample set based on the mapping relationship, and training a question generation model through the sample set until a target question generation model meeting a training stop condition is obtained.

The invention discloses a translation model training method, a text translation method and a related device, and relates to the technical field of translation, and the method comprises the following steps: carrying out professional term marking on each source language text in a source language text set to obtain a term marking text corresponding to each source language text, and carrying out back translation on the term marking text by utilizing an initial translation model to obtain a translation model; the method comprises the steps of obtaining a back-translated text marked with back-translated terms, determining term back-translating accuracy of the back-translated terms in the back-translated text relative to professional terms, determining a first source language text enabling the term back-translating accuracy to be smaller than an accuracy threshold value from a source language text set, obtaining a standard translated text of the first source language text, and obtaining the standard translated text of the first source language text. And forming a training set with the first source language text to perform fine tuning training on the initial translation model to obtain a target translation model. According to the method, the technical terms with non-ideal translation effects are accurately positioned based on the term back translation accuracy, and then the technical terms are subjected to targeted training, so that the translation accuracy of the model is improved.

A computer-implemented method is disclosed, comprising: receiving, by one or more large language models, LLMs, one or more natural language system prompts which, when processed, causes the one or more LLMs to perform a system method. The system method comprises: detecting the source language of a source text, wherein the source text is provided in natural language; determining, based on a user prompt, a target language; generating, a first translation by translating the source text from the source language to the target language; generating, a back translation by translating the first translation from the target language to the source language; comparing, the back translation with the source text to determine one or more inconsistencies; determining whether the one or more inconsistencies exceed an error threshold; based on the one or more inconsistencies exceeding the error threshold, generating, based on the one or more inconsistencies, a second translation by translating the source text from the source language to the target language; and based on the one or more inconsistencies not exceeding the error threshold, outputting, to a user, the first translation; processing, by the one or more LLMs, the one or more system prompts; receiving, by the one or more LLMs, via a user device, a user prompt comprising the source text; and providing, by the one or more LLMs, using the system method, the first translation or the second translation.

This invention discloses a sliding table saw, including a feeding rack, a stopper mounted on the feeding rack, and further including a front-to-back translation device, a swing angle device, a left-to-right sliding device, and a quick-locking device. The front-to-back translation device includes two translation guide rails fixed on the feeding rack, a translation slider slidably mounted on the two translation guide rails, and a translation plate fixedly mounted on the translation slider. There are four quick-locking devices, two of which are located at the front ends of the feeding rack and the other two are located at the rear ends of the feeding rack, used to lock two fixed shafts located at the bottom ends of the stopper. The stopper of this invention can be translated front-to-back, slid left-to-right, and rotated, and can also be quickly locked. It is convenient to use and suitable for processing boards of different specifications.

Provided are a multi-modal model training method, apparatus and device, and a storage medium. The method includes the following steps: obtaining a training sample set, and training a multi-modal model for a plurality of rounds by successively using each of training sample pair in the training sample set: during use of any one of the training sample pairs for training, obtaining an image feature of a target visual sample firstly, and then determining whether back translation needs to be performed on a target original text; when back translation needs to be performed on the target original text, performing corresponding back translation to obtain a target back-translated text, and obtaining a text feature of the target back-translated text; and training the multi-modal model based on the image feature and the text feature.

Embodiments of the present application disclose a parallel corpus data pair construction method and device and a storage medium. The method comprises: inputting first corpus data with first style characteristics into a first style conversion model to obtain second corpus data with second style characteristics; the first style conversion model is obtained based on back translation training; inputting the first corpus data into a second style conversion model to obtain third corpus data with the second style characteristics; the second style conversion model is obtained based on adversarial training; calculating a first score corresponding to the second style characteristics of the second corpus data; calculating a second score corresponding to the second style characteristics of the third corpus data; if the first score is greater than the second score, constructing a first parallel corpus data pair using the second corpus data and the first corpus data; if the first score is not greater than the second score, constructing the first parallel corpus data pair using the third corpus data and the first corpus data, thereby solving the problem of scarcity of parallel corpus data pairs.