146 results about "Machine translation" patented technology

The invention discloses an artificial intelligence speech recognition system, and the system comprises a multi-modal feature extraction module which employs an improved Conformer architecture to synchronously extract the time-frequency features and text embedding vectors of speech signals; the joint training module is used for performing joint optimization on ASR and NMT loss functions through an adversarial training strategy, learning voice recognition and machine translation tasks at the same time through joint training, and completing direct mapping from voice features to a target language; the context perception translation engine is used for integrating an attention mechanism of a pre-training language model, carrying out deep coding on the extracted speech features and generating cross-language semantic representation; the self-adaptive post-processing module is used for dynamically optimizing an output result by adopting a reinforcement learning framework, dynamically adjusting the output result according to a reward function, and optimizing translation quality and a speech synthesis effect; the dynamic language recognition module is a real-time language classifier based on a Wave2Vec 2.0 framework and is used for recognizing the language of the input voice in real time; and the incremental field adaptation module is used for quickly updating a field term library by using a LoRA fine tuning technology.

The invention provides a PDF (Portable Document Format) optimized translation method based on intelligent text detection, which comprises the following steps of: 1, analyzing a PDF document, respectively identifying a text region and an image region in the PDF document, and extracting a native text block of the text region in the PDF document and structured position information of the native text block; the recognition result and the native text block are combined into a unified structured data set; 3, performing machine translation by adopting a window translation strategy in combination with the context, and extracting a target translation; adjacent text blocks are dynamically associated during translation, and a target text range is defined through boundary markers; and 4, dynamically optimizing the text layout according to the target translation length, and backfilling a translation result to an original position. According to the method, the translated text can be accurately and seamlessly backfilled to the corresponding position of the original image, meanwhile, the content of the original image is ensured not to be shielded, and visual disorder or information loss caused by text coverage is avoided.

The invention relates to a natural language processing and multi-language data processing technology, and discloses a multi-language corpus automatic construction and translation optimization system which comprises a corpus collection module, a language recognition and grouping module, a semantic alignment module, a translation optimization module and a corpus quality evaluation and screening module. According to the system, multi-language text data can be automatically collected from the Internet, language recognition and structured storage are carried out, semantic vector coding and alignment matching are carried out on sentences of different languages through a cross-language pre-training model, and high-quality parallel corpus generation is achieved. Meanwhile, the translation model is subjected to incremental training and language proportion regulation and control by utilizing the aligned corpora, so that the translation performance is improved; corpus quality is automatically evaluated and screened through a scoring mechanism, and data reliability is guaranteed. The system can be widely applied to the fields of multi-language machine translation, cross-language information retrieval, intelligent corpus construction and the like.

Provided is a pseudo parallel translation data generation apparatus for generating pseudo parallel translation data for accurately performing machine translation in an adaptation target domain even when there exists no parallel translation data for the adaptation target domain. Using other-domains parallel translation data D0(L1-L2), other-domains first language data D0(L1), other-domains second language D0(L2), adaptation target domain first language data D0(R1), and adaptation target domain second language data D0(R2), the pseudo parallel translation data generation apparatus 100 performs optimization processing for a cross-lingual language model including an input data embedding unit 2 and an XLM processing unit 3, and performs parameter optimization processing for a pseudo parallel translation data generation NMT model including the input data embedding unit after the optimization processing and a machine translation processing unit 5. Performing processing using the pseudo parallel translation data generation machine translation model obtained by the parameter optimization processing allows for obtaining pseudo parallel translation data for the adaptation target domain for which no parallel translation data sets exist.

The invention discloses a cross-border logistics document multi-language machine translation method based on natural language processing, and relates to the technical field of machine translation, and the method comprises the steps: collecting original data of a cross-border logistics document, and carrying out text extraction and preprocessing through an OCR technology; performing language recognition and format analysis on the preprocessed document text to determine a source language and a target language; using a pre-trained cross-language alignment translation model to translate the document text into a target language text; the translation result is input into a compliance auditing module, and automatic compliance auditing is conducted through a knowledge graph and a double-check algorithm; and generating an output containing a translation result and a compliance audit report, and supporting manual review and revision. According to the method, the translation accuracy of the documents can be effectively improved, the manual auditing burden is reduced, and the globalization requirements of cross-border e-commerce and logistics enterprises are met.

The invention discloses a machine translation method and system based on large model fusion task disassembly and cognitive planning. The method comprises the following steps: 1) decomposing a translation task into a plurality of stages, including syntactic analysis, semantic alignment, term precise mapping, context coherence optimization and culture adaptation; a cue word used in the semantic alignment stage prompts a model to accurately recognize semantic connotation of a source language sentence, and finds a most matched semantic expression in a target language; and 2) in each stage, guiding the large model to execute a corresponding translation task by using a specific cue word so as to ensure the translation quality. According to the method, through a multi-stage cognitive translation instruction framework, a context-aware multi-round dialogue engine and an interpretable translation decision tracing mechanism, the quality and efficiency of machine translation are remarkably improved, good adaptive capacity and interpretability are achieved, and the method is suitable for translation requirements of various complex texts.

The invention provides an intelligent glasses independent communication method and system based on RedCap and eSIM and a medium, and the method comprises the steps: after intelligent glasses are started, initiating a registration request to a 5G core network based on an embedded eSIM chip, and after the core network completes identity authentication and authentication, distributing an independent IP address for the intelligent glasses; collecting interaction data, and encrypting the interaction data; performing data processing on the interaction data based on a cloud AI platform to generate result data; analyzing the result data based on voice recognition and a machine translation model to obtain AR content or an output text or a voice result; the signal intensity of the 5G core network is monitored in real time, the signal intensity is compared with a set intensity threshold value, and a communication mode is dynamically switched based on a comparison result; the intelligent glasses are completely independently networked, free movement and use of the intelligent glasses are achieved, real-time translation, AR navigation, remote cooperation and cloud cooperation application are supported, and user experience and scene adaptability are improved.

This invention discloses a low-power edge-side simultaneous interpretation system and method based on audio-text synchronization and visual feature fusion, relating to the fields of multimodal human-computer interaction and machine translation technology. The system achieves high-precision audio-text timing alignment through an audio-text synchronization matching module, generates a lightweight visual feature stream using a visual feature processing module, and performs spatiotemporal fusion by a multimodal fusion inference module. Combined with edge-side heterogeneous computing power scheduling and dynamic power consumption control, it significantly reduces the power consumption of edge devices while ensuring low-latency translation of ≤20ms per frame. This invention fills the technological gap in edge-side low-power multimodal simultaneous interpretation and can be widely applied in edge scenarios such as mobile office, international communication, and smart wearables.

The present invention relates to the field of billet continuous casting production equipment, and provides a continuous casting machine translation control system and a continuous casting machine. The continuous casting machine translation control system includes a first hydraulic cylinder, a second hydraulic cylinder, a first hydraulic lock, a second hydraulic lock, a first rodless cavity oil supply pipeline, a second rodless cavity oil supply pipeline, a first rod cavity oil supply pipeline, a second rod cavity oil supply pipeline, a control system, a main control mechanism, and an auxiliary control mechanism. The control system is configured to control the main control mechanism to move the first hydraulic cylinder and the second hydraulic cylinder, and is configured to receive signals from a first position sensor and a second position sensor. When the signals indicate that the moving speeds of the two hydraulic cylinders are inconsistent, an instruction is sent to the auxiliary control mechanism to make the moving speeds of the two hydraulic cylinders consistent. The continuous casting machine includes the above-mentioned translation control system. The continuous casting machine translation control system provided by the present invention can improve the problem of the asynchronous moving speeds of the two hydraulic cylinders, and the continuous casting machine provided with the control system has good stability.

The invention discloses a translation hardware acceleration platform and method based on an SGRU, and relates to the technical field of machine translation, and the platform comprises an accelerator which comprises a control module, an input and output module, an Embedding layer, a distributed pulse cache module, a model parameter cache module, a calculation module and a gating calculation core; the input and output module is respectively connected with the control module, the Embedding layer, the model parameter cache module and the PC terminal; the calculation module is respectively connected with the control module, the Embedding layer, the distributed pulse cache module and the model parameter cache module; the gating calculation core is connected with the control module and the distributed pulse cache module. A calculation heterogeneous design of a calculation module and a gating calculation core is adopted, data conflicts are avoided through distributed caching, dynamic loading and updating are carried out based on a model parameter caching module, overall control is carried out according to an SGRU-based Seq2Seq model, and high-performance and low-power-consumption translation can be realized.

The application provides a knowledge learning method and device of a machine translation model, comprising: constructing a migration dataset; respectively using a machine translation teacher model and a machine translation student model to translate the original text of each sample of the migration dataset to obtain the first candidate word probability distribution and the second candidate word probability distribution of each sample of the migration dataset; determining the migration loss of each sample of the migration dataset according to the first candidate word probability distribution, the second candidate word probability distribution and the standard candidate word probability distribution of each sample of the migration dataset; and realizing the knowledge learning of the machine translation student model to the machine translation teacher model based on the first candidate word probability distribution of each sample of the migration dataset and the migration loss. The application enables the machine translation student model to learn complementary knowledge from the machine translation teacher model through the migration loss, realizes the knowledge accumulation of the machine translation student model, and further improves the translation performance of the machine translation student model.

The application discloses a biological data processing method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring atomic attribute information of a first atom contained in a first molecular substance and acquiring atomic attribute information of a second atom contained in a second molecular substance; inputting the atomic attribute information of the first atom and the atomic attribute information of the second atom into a machine translation model; in the machine translation model, generating a first reaction intensity value between the first atom and each atom in an atomic set and a second reaction intensity value between the second atom and each atom; the atomic set comprises the first atom and the second atom; and predicting a substance affinity between the first molecular substance and the second molecular substance according to the first reaction intensity value between the first atom and each atom and the second reaction intensity value between the second atom and each atom. The application can improve the accuracy of the obtained substance affinity.

The present disclosure provides a method for knowledge distillation, an apparatus, an electronic device, a medium and a computer program product. The method includes: acquiring a training source text and a standard translation text corresponding to the training source text; inputting the training source text into a teacher translation model and a student translation model separately, to obtain a teacher distribution output by the teacher translation model and a student distribution output by the student translation model; obtaining a standard translation distribution according to the standard translation text and the training source text; and performing iterative training on the student translation model according to the teacher distribution, the student distribution, and the standard translation distribution, to obtain a target machine translation model.

The application discloses a machine translation software defect detection method based on combined semantics, comprising the following steps: S1, obtaining different Chinese translation sentences of the same English source sentence from different translation software; S2, using a word alignment model to correspond English words in the source sentence with segmented Chinese words in the Chinese translation sentences; S3, using a sentence compression model and a syntactic structure analysis method to obtain a main part and each additional part of the English source sentence respectively and form a sub-sentence set; S4, aligning each part of the English source sentence obtained in the step S3 with the corresponding translation part to obtain aligned translation of the main part and the additional part of the source sentence; and S5, detecting errors, including semantic similarity calculation and synonym checking.

The disclosure provides a language model training method and a translation method for multilingual translation, and relates to the technical field of artificial intelligence, in particular to the fields of natural language processing, deep learning, machine translation and the like. The language model training method for multilingual translation comprises: obtaining a sample text pair, the sample text pair comprising a first text in a first language and a second text in a second language, the semantics of the first text being the same as the semantics of the second text; inputting the first text and the second language into a language model to obtain a first translated text in the second language output by the language model; inputting the second text and the second language into the language model to obtain a second translated text in the second language output by the language model; determining a loss value of the language model based on a first difference between the first translated text and the second text and a second difference between the first translated text and the second translated text; and adjusting parameters of the language model based on the loss value.

Described is a system and method for training a multilingual semantic parser. A method includes receiving, by a multilingual semantic parser, a multilingual training dataset, wherein the multilingual training dataset includes pairs of utterances and meaning representations from at least one high-resource language and at least one low-resource language and wherein the multilingual training dataset is initially a machine-translated dataset, training, the multilingual semantic parser, by translating the utterances in the multilingual training dataset to a target language; and iteratively performing selecting, by an acquisition functions estimator, a subset of the multilingual training dataset for human translation, updating the multilingual training dataset with the human-translated subset of the multilingual training dataset with, and retraining, the multilingual semantic parser, with the updated multilingual training dataset.

The invention discloses an ancient modern text machine translation method based on deep learning, and belongs to the technical field of natural language processing. The invention provides a multi-task collaborative optimization framework aiming at the problems of domain knowledge deficiency, frequent word activation, corpus scarcity and the like of an existing neural network model in ancient language translation. Precise domain knowledge injection is realized by constructing a closed ancient language parallel corpus retrieval library and combining historical term alignment and logic chain reasoning; a probability weighted part-of-speech embedding mechanism is adopted, and grammar feature distribution of virtual word ambiguity and word type activity is dynamically fused, so that the grammar analysis robustness is improved; and semantic generation, retrieval alignment and part-of-speech constraint tasks are jointly optimized through a fixed weight strategy. According to the method, the semantic accuracy and the logic continuity of ancient language translation are improved, and extended application of complex ancient language styles is supported. Experimental results verify that the performance of the method is improved in terms of BLEU and ROUGE-L indexes, and efficient technical support is provided for ancient book digitization and cross-time explanation.

The application provides a kind of two-stage visual fusion multimodal translation method and system, to solve the problem of insufficient utilization of visual information in existing multimodal translation, single fusion mechanism, model prone to ignore visual information.The method comprises: obtaining source language text and corresponding image, extracting fine-grained regional visual features and coarse-grained global visual features;In the first stage, the fine-grained regional features are spliced with the text representation and encoded through guided self-attention to realize cross-modal alignment;In the second stage, the coarse-grained global features are projected and broadcasted through gated residual modulation to inject scene-level prior information;The multimodal machine translation target and the visual conditional mask language modeling target are jointly optimized for training.The application effectively improves the translation quality through two-stage multi-granularity fusion, and the BLEU and METEOR indicators on Multi30K and other data sets are significantly better than existing methods, which can be applied to multimedia translation, cross-language retrieval and other fields.

The invention provides a multi-head attention processing method, a related device and a medium, and the method comprises the steps: dividing an input matrix into a first number of sub-blocks, and carrying out the first linear transformation through a first number of processing units, and obtaining a first number of first transformed sub-blocks; performing sub-block transformation on a first number of first transformed sub-blocks corresponding to the first number of processing units to obtain a second transformed sub-block corresponding to each processing head, and inputting the second transformed sub-block into a processing head corresponding to the second transformed sub-block for attention calculation to obtain a first attention result sub-block; and performing sub-block inverse transformation on the first attention result sub-blocks corresponding to the processing heads to obtain second attention result sub-blocks corresponding to the processing units, and generating a multi-head attention result matrix based on the second attention result sub-blocks. The multi-head attention processing efficiency can be improved, and the multi-head attention processing method and device can be applied to scenes such as artificial intelligence assistants and machine translation.

The invention, which relates to the technical field of English parsing, discloses an intelligent parsing system for a long and difficult English sentence structure in combination with context features, comprising a context feature hierarchical extraction module, a dynamic coupling association module, an adaptive weight adjustment module and a syntactic structure parsing module. According to the method, a three-layer context feature layered extraction model of a syntactic structure layer, a chapter semantic layer and a sentence pattern function layer is constructed, the multi-dimensional context features of English long and difficult sentences are dynamically coupled and associated, and the feature analysis weight of each layer is optimized in real time according to the context feature distribution through an adaptive weight adjustment module; synchronous linkage of syntactic structure analysis and context semantics is achieved, the problems that in the prior art, due to the fact that multi-dimensional context association is ignored, nested subordinate sentence splitting errors, logic confusion of master and slave sentences, core component positioning deviation and the like are caused are solved, and the accuracy of English long-difficult sentence structure analysis is improved; and a technical support is provided for application scenes depending on long and difficult sentence analysis, such as machine translation, academic literature research and reading, language teaching and the like.

The invention relates to the technical field of natural language processing and artificial intelligence application, and discloses a multilingual spelling error correction method based on big language modeling, which comprises the following steps: generating multilingual training data through a disturbance strategy, and supervising and finely tuning a big language model; zero sample error detection is realized based on lexical-level probability deviation; and a detection result and a generative error correction result are integrated, and only consistent errors are replaced. According to the method, the excessive error correction tendency of the generative model is effectively inhibited, the error correction performance of low-resource languages such as Vietnamese and Indonesia is improved, and meanwhile, the principle of'minimum change 'is kept. The method does not need manual data annotation, has good mobility and cross-language adaptability, and can be widely applied to scenes such as text cleaning, language learning and machine translation.

The application provides a translation method, system, medium, equipment and program product based on a semantic energy conservation network, belonging to the technical field of machine translation. In the technical solution, the vocabulary of the input source language sentence is represented in the form of energy, and from the perspective of energy conservation, a Hamilton deep neural network (HDNN) and a VI Verlet integrator are used as an energy evolution network to process the evolution of the vocabulary position and active quantity information. The relative position and active quantity after evolution are input into a translation network based on a Transformer to obtain a translation result. The Hamilton total energy of the sentence is recalculated through an energy evaluation network, and then the energy difference is calculated to evaluate the translation loss. This method combines the ideas of Hamilton dynamics and the advantages of deep learning, evolves through semantic energy conservation, uses HDNN for vocabulary information evolution and feature extraction, ensures the stability and consistency of the information, and enables the model to better understand the context relationship of the input text, which helps to improve the translation accuracy.

The invention discloses a multilingual speech semantic intelligent translation method and system applied to an exhibition scene, and belongs to the technical field of machine translation, and the method comprises the following steps: S1, obtaining a multi-person question judgment result; s2, if the multi-person questioning judgment result is multi-person questioning, audio identification information is obtained through analysis, and otherwise, the audio identification information is directly obtained through analysis; s3, obtaining each storage question keyword, each contrast question keyword and a key matching weighting factor corresponding to each question keyword; s4, obtaining a same-group evaluation result, if the same-group evaluation result is the same group, analyzing to obtain the comprehensive matching similarity of the storage groups, otherwise, analyzing the comprehensive matching similarity of each parallel storage group; s5, obtaining a comprehensive matching judgment result, if the comprehensive matching judgment result is unqualified, performing secondary refining processing to obtain a question and answer, and otherwise, directly obtaining the question and answer; and S6, voice broadcasting is carried out, and accurate separation and language recognition of voice sources of different questioning users are achieved.

The application discloses a machine translation quality estimation method based on multi-semantic space, which is divided into machine translation quality estimation model training and machine translation quality estimation. The model training steps are as follows: generating pseudo reference translation and back translation by using a dialogue large language model; extracting machine translation quality features in the source language semantic space; extracting machine translation quality features in the target language semantic space; extracting machine translation quality features in the cross-language semantic space; extracting a machine translation quality feature vector in the multi-semantic space; predicting a machine translation quality score based on the multi-semantic space; and training a machine translation quality estimation model based on the multi-semantic space. The machine translation quality estimation method steps are as follows: normalizing a source language sentence and machine translation to be quality estimated; inputting the normalized source language sentence and machine translation into the trained machine translation quality estimation model based on the multi-semantic space, and predicting a machine translation quality score.

This invention relates to a prompt-based machine translation method, belonging to the field of natural language processing technology. It solves the problems of inaccurate, omitted, and mistranslated nouns and proper nouns in existing machine translation models. By constructing a set of nouns and their translations from the text to be translated, the input text and adjustment matrix of the translation model are obtained. The translation model is then used to translate the input text, and the attention calculation of the model is adjusted using the adjustment matrix M, ultimately outputting the translation. Based on the input data containing noun translation prompts and the adjustment matrix, the accuracy of the noun translation by the translation model is guaranteed to a certain extent, solving the problems of omitted and mistranslated nouns, and improving the accuracy of noun translation in machine translation models.

The application provides a translation terminology word form adaptive replacement method and device and electronic equipment. The method comprises the following steps: performing terminology replacement processing on original translation text of to-be-translated text to obtain synthesized translation text; inputting the synthesized translation text into a first translation terminology replacement model to obtain target translation text corresponding to the to-be-translated text output by the first translation terminology replacement model; the first translation terminology replacement model is obtained by training according to synthesized translation text samples and corresponding real translation text labels; the synthesized translation text samples are obtained by performing terminology replacement processing on original translation text samples. The application can automatically generate translation with correct terminology word forms, ensure the correctness of the final translation grammar, realize high-quality translation effect in the face of text translation scenarios containing more specific field professional terms, and effectively improve the quality of machine translation.

A scalable, automated timed text workflow system designed to optimize the generation and refinement of time-synchronized textual content for video is disclosed. Integrated Machine Translation (MT) models and advanced AI-driven tools such as Computer Vision, Generative AI, and Traditional AI automatically generate and refine timed text, including subtitles, closed captions (CC), and SDH (Subtitles for the Deaf and Hard of Hearing). Human review is coordinated through a workflow orchestration system when needed, offering flexibility and scalability for handling high-volume media processing across various platforms and formats.

The invention belongs to the technical field of machine translation, and provides an instant intelligent translation method based on a clinical doctor-patient double-screen translation machine and the translation machine. The method comprises the steps that a diagnosis and treatment session is established, a medical care screen, a patient screen and a translator host are bound, and session parameters are initialized; a double-screen synchronization relation and a time axis are maintained, and texts, audios and visual data are arranged in a unified mode; according to a doctor-patient speaking automatic switching mode, respectively displaying a source language and a translated text on double screens and synchronously broadcasting, keeping session continuity and labeling a speaking role; calling a medical special translation engine and a term library, and performing term disambiguation in combination with the medical record and the shared content; visual data sharing is controlled by the medical care screen, synchronous mirroring display is carried out on the patient screen after anchoring with statement fragments, and accurate alignment of images, texts and sounds on the same time axis is achieved. According to the invention, the problems of mismatching of multi-terminal sessions, non-synchronization of cross-modal contents, inaccurate medical term translation and difficulty in visual sharing are solved, and the accuracy and efficiency of clinical doctor-patient communication are improved.

To improve a filtering-type example sentence-driven analysis unit forming one input sentence analysis tree with using, as one input, an OR tree obtained by expressing a plurality of analysis trees obtained through analysis of an input sentence with use of grammar rules in the form of one tree structure and also with using, as the other input, a temporarily exclusive tree obtained by the example sentence or an exclusive three pre-equipped by the apparatus.SOLUTION: In a common word sequence acquisition between an input sentence and an example sentence, a method according to the present invention includes the steps of: acquiring a plurality of common word sequences; designating the minimum number of common words; determining a common word start position in an example sentence tree; and replacing the common word with a common part of speech.SELECTED DRAWING: Figure 1

The application discloses a neural machine translation Internet of Things remote proof method for data flow attacks, which comprises an offline stage and a runtime verification stage. In the offline stage, a verifier and a prover first complete the negotiation of a symmetric key for subsequent remote proof, and at the same time, complete static plugging for a target program. A program control flow dataset is pre-constructed through fuzzy testing, a neural machine translation model is trained to establish the mapping of program input to an execution path, and a control flow graph is embedded to provide a structured prior for decoder attention. In the runtime verification stage, the verifier initiates a proof challenge to the prover, the prover provides the program input and the control flow path of the last execution, the verifier predicts a benign path from the program input by the neural machine translation model, and the difference between the benign predicted path and the actual path is used to judge the legitimacy of the prover. The application realizes accurate modeling of the program execution path, and shows effective detection capability for the abnormal path triggered by malicious input containing real vulnerabilities, and a good balance is achieved between detection coverage and running overhead.