15results about How to "Improve translation performance" patented technology

The embodiment of the invention discloses a model training method and device, electronic equipment, a medium and a program product, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent traffic, auxiliary driving and the like. According to the embodiment of the invention, a training sample set is obtained, wherein the training sample set comprises an initial sample text corresponding to an initial scene and a newly-added sample text corresponding to a newly-added scene; translating the initial sample text through a teacher model to obtain a teacher translation result; translating the initial sample text and the newly added sample text through an initial student model to obtain a student translation result; calculating a loss value by combining the teacher translation result and the student translation result; and on the basis of the loss value, updating model parameters of the initial student model to obtain a trained student model, so that the trained student model is used for translating the to-be-translated text corresponding to the initial scene and the newly-added scene. Therefore, according to the scheme, the multi-scene adaptive capacity of the model can be enhanced while the model training efficiency is improved.

The present invention discloses a machine translation method using part-of-speech information, which is characterized by using a training set of bilingual corpora containing corresponding part-of-speech tags, and using part-of-speech information to assist machine translation after the translation model training is completed; the translation model includes a shared encoder, a decoder and a part-of-speech classifier, and the part-of-speech information is imported in different ways to enhance the model's language understanding and text generation capabilities; the shared encoder is composed of a source sentence encoder and an auxiliary sentence encoder; the decoder includes an original multi-head attention module and an auxiliary multi-head attention module. Compared with the prior art, the present invention has the function of improving the performance of the machine translation model by introducing part-of-speech information. The present invention uses the Transformer as the basic translation model, and imports part-of-speech information in different ways in the encoder and decoder components, so that the translation model can use the part-of-speech information to enhance the model's language understanding and text generation capabilities, thereby improving the quality of machine translation.

The application provides a translation method and device, wherein the translation method comprises: obtaining a sentence to be translated; inputting the sentence to be translated into a translation model to obtain a translated sentence corresponding to the sentence to be translated, wherein the translation model comprises an encoding layer and a weight layer, the encoding layer comprises at least one first stack layer, the first stack layer comprises a multi-head attention mechanism sublayer and a feedforward network layer, and the weight layer is used to generate a weight vector according to at least one encoding vector output by the encoding layer and a weight coefficient corresponding to each encoding vector, and the weight vector is used to generate the translated sentence. Since the translation model comprises the multi-head attention mechanism sublayer, richer information can be obtained by introducing the attention mechanism, so that the weight corresponding to each first stack layer is adjusted, the architecture of the translation model is optimized, and the translation effect of the sentence to be translated is further improved in the use process of the translation model.

Embodiments of the present application provide a translation method and device, electronic equipment and computer readable storage medium, and relate to the fields of artificial intelligence, natural language processing, multimedia and cloud technology. The method comprises: obtaining a source language text to be translated, and translating the text to be translated by using a trained translation model to obtain a target text corresponding to the target language of the text to be translated; the translation model is obtained by the following method: obtaining a plurality of training samples comprising a first text in a source language and a second text corresponding to the target language of the first text; determining the training loss weight corresponding to each sample according to the position bias of each word pair matched in the first text and the second text of each sample in the two texts; and iteratively training an initial neural network model based on the plurality of samples and the training loss weights corresponding thereto to obtain the translation model. Based on the method, the translation effect can be effectively improved.

The present application provides a sentence translation method, device, equipment and medium, which relates to the field of machine translation technology and is used to improve the fidelity of translation results. The method includes: simulating the overconfident translation process of the first translation model with the help of a first translation auxiliary model, thereby quantifying the fidelity training loss of the first translation model according to the difference between the probabilities output by the first translation auxiliary model and the first translation model, and directly and effectively adjusting the model parameters of the first translation model based on the fidelity training loss, thereby improving the translation fidelity of the translation sentence output by the trained target translation model.

The invention provides a multi-modal neural machine translation method for scene self-adaption based on visual fantasy guidance, and a scene self-adaption translation function is realized by fusing different modal features and continuously reducing differences among modals by utilizing visual fantasy as guidance. According to the method, an asymmetric alternative updating strategy is introduced, so that the modal gap is reduced, and the characterization capability of the multi-modal data is improved. And finally, establishing a cross-modal adaptive fusion mechanism to effectively fuse the two modals. According to the method, the multi-modal representation learning ability of the model in a complex and changeable image-text environment can be effectively improved, and it is ensured that the model can still maintain high machine translation performance even in a noisy data or plain text scene in the reasoning stage. According to the method, the optimal score superior to that of other prior art is achieved on most sets, it is proved that the method shows outstanding stability and consistency in various scenes, especially in a noise environment, and the machine translation performance is powerfully improved.

This invention provides a document translation method, apparatus, device, and storage medium. The document translation method includes: acquiring a source language document to be translated; acquiring document attribute information corresponding to each sentence in the source language document, wherein the document attribute information is information determined based on the corresponding sentence and / or its context sentences to assist in determining the meaning of words in the corresponding sentence within the source language document; and translating each sentence in the source language document using the document attribute information corresponding to each sentence, thereby obtaining a target language translation of the source language document. The document translation method provided by this invention has good document translation performance.

The present invention relates to a virtualization nested translation method, system and readable medium, comprising receiving a virtual address translation request, judging the address translation mode of the virtual address translation request, and determining the virtual address type; selecting a corresponding translation cache according to the determined virtual address type; performing address translation in a multi-level translation cache in a parallel or serial search mode under the selected translation cache to obtain a search result; judging whether the search result hits a leaf item of a page table; if the search result hits a leaf item, returning a corresponding physical address to complete the translation; if the leaf item is not hit, performing a page table traversal to obtain a physical address. The method can improve the speed of virtualization nested translation, reduce redundant virtual addresses of a translation cache and redundant PTEs of a translation path cache under a single technical condition, and improve the translation delay from a virtual address to a physical address in memory virtualization or peripheral virtualization.

The invention discloses an oil and gas geological survey professional vocabulary translation and analysis method and a system thereof, a dynamically updated database is established, the database stores a corresponding relationship between professional vocabularies and corresponding translation explanation contents, and a translation model is trained based on the database; explaining complex professional vocabularies into popular and easy-to-understand contents by utilizing the translation model; screening out keywords from the interpretation translation content; and constructing a knowledge base, matching analysis contents for the professional vocabularies from the knowledge base according to keywords in the interpretation and translation contents, and outputting the matched analysis contents, so that the professional vocabularies in the oil and gas geology field can be accurately identified and translated, complex professional terms are converted into popular and easy-to-understand languages, and the interpretation and translation of the professional vocabularies in the oil and gas geology field can be realized. By performing secondary analysis on the explained characters, related documents can be more accurately matched from the knowledge base, concise and clear answers are formed, the conditions of mismatching and missed matching are reduced, and secondary explanation which is easier to understand is formed.

The application discloses a multilingual neural machine translation system using language family information, which is constructed based on a Transformer basic model, and a language family information module is added after each self-attention mechanism module and a feedforward neural network module in the Transformer basic model; the language family information module inputs language family information and the output of a preceding module, and outputs a vector after the language family information is fused. The application further provides a multilingual neural machine translation method using language family information. The multilingual neural machine translation system using language family information provided by the application alleviates the performance decline problem of large-scale multilingual machine translation on high-resource languages, and further improves the translation performance on low-resource languages.

The embodiment of the invention discloses a front-end page processing method and a related product. The front-end page processing method comprises the following steps: determining a text on a front-end page and translation attribute information of the text; determining a first interface from a plurality of interfaces according to the translation attribute information; the plurality of interfaces are pre-integrated in an application program corresponding to the front-end page, and the first interface is an interface with interface information matched with the translation attribute information; and calling the first interface to translate the text to obtain a text translation result of the text, and displaying the front-end page based on the text translation result. According to the method, the instantaneity, the accuracy and the flexibility of text translation in the front-end page are improved, and the instantaneity requirements of frequently updated network applications and dynamically changed contents on translation can be met.

This invention provides a high-throughput and efficient method for screening multiple target genes that can regulate mRNA expression levels in cells by co-transfecting or sequentially transfecting mRNA with a gene silencing library targeting genes in cells using lipid nanoparticles. Several target genes that can regulate mRNA protein expression levels through the action of siRNA have been discovered. By manipulating these genes that regulate mRNA expression levels, the translational capacity of mRNA in mRNA vaccine and drug development can be improved, mRNA protein expression efficiency can be increased, and immunogenicity and toxicity can be reduced.

This invention relates to the field of machine translation technology, and particularly to a vertical domain translation model training method and storage medium. The translation model employs an encoder-decoder architecture. The training method involves the following steps: first, inputting bilingual text into the translation model and training sentence vectors; then, fine-tuning the trained translation model using high-resource vertical domain data, which consists of high-resource sentences; next, retrieving high-resource sentences using low-resource sentences, and inputting both the low-resource sentences and the retrieved high-resource sentences into the encoder for cross-language multi-instance fusion to obtain the decoder's output state; finally, performing contextual nearest neighbor retrieval based on the decoder's output state to obtain the translation output probability distribution. In essence, this translation model training method utilizes high-resource vertical domain data to enhance low-resource vertical domain data, thereby achieving better translation results.

A language translation method and an electronic device are provided, and relate to the field of terminal technologies, to improve language translation quality. The method includes: The electronic device obtains a word distribution of a translation result of a first sentence in a source language and a second sentence in the source language, and obtains a word distribution of a first translation result of the second sentence and a jump rate of a word included in the first translation result of the second sentence. Then, the electronic device determines a word distribution of a second translation result of the second sentence based on the word distribution of the translation result of the first sentence, the word distribution of the first translation result of the second sentence, and the jump rate. The second sentence includes a word in the first sentence and a first word. The jump rate is related to a first similarity. The first similarity is a similarity between the translation result of the first sentence and the first translation result of the second sentence.

This invention discloses a machine translation method, system, device, and storage medium for Dong language under low-resource conditions, belonging to the fields of natural language processing and machine translation technology. It includes: a Dong language corpus acquisition and preprocessing module, a low-resource data augmentation module, a multilingual transfer training module, a sample balancing training module, and a translation generation and result output module. This invention improves the model's learning ability and translation performance under low-resource conditions, enabling the model to leverage knowledge from other languages ​​to enhance its ability to model the semantic and syntactic features of Dong language, increasing the effective weight of Dong language samples during training, thereby improving Dong language translation performance and enhancing the usability and promotional value of the Dong language translation system in practical application scenarios.