7results about How to "Improve translation performance" patented technology

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.

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 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.

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.

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.