275 results about "Machine translation" patented technology

In one embodiment, a method of training one or more artificial intelligence (AI) models for language-based communication prompts with a service provider is disclosed. The training method includes generating industry specific labels used to fine tune a large language model; providing an industry specific database associated with the industry specific labels to fine tune the large language model; reading the industry specific database into the large language model; adjusting the parameters of the large language model to recognize industry specific terms associated with servicing equipment within the industry; adjusting the parameters of the large language model to discover the intent associated with the industry specific terms; and adjusting the parameters of the large language model for industry specific tasks including questions and answer tasks, named entity recognition, classification tasks, and machine translations tasks.

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.

Disclosed are embodiments for improving training data for machine learning (ML) models. In an embodiment, a method is disclosed where an augmentation engine receives a seed example, the seed example stored in a seed training data set; generates an encoded seed example of the seed example using an encoder; inputs the encoded seed example into a machine learning model and receives a candidate example generated by the machine learning model; determines that the candidate example is similar to the encoded seed example; and augments the seed training data set with the candidate example.

The invention relates to the technical field of natural language processing. The method for translating the multi-source data of the traditional Chinese medicine based on deep learning comprises the following steps: acquiring related data of the traditional Chinese medicine to obtain an initial data set of the traditional Chinese medicine; formatting the traditional Chinese medicine initial data set to obtain a formatted data set; performing term annotation and cultural background annotation on the formatted data set to obtain an annotated data set; performing data cleaning on the labeled data set to obtain a preprocessed data set; performing data enhancement on the preprocessed data set; constructing an initial translation model, and training the initial translation model to obtain a pre-trained translation model; verifying the pre-training translation model, finely adjusting the pre-training translation model according to a verification result, testing the finely-adjusted pre-training translation model, and evaluating a test result; and according to an evaluation result, optimizing the pre-trained translation model after fine tuning to obtain a translation optimization model. The problems that an existing machine translation tool is not accurate enough in translation result and poor in cultural adaptability are solved.

To provide a translation support device and a program that can obtain natural translated sentences using designated words as designated translation words.SOLUTION: A translation support device for acquiring a sentence generation processing result using generation model information in a machine-learned state to generate a sentence based on an instruction in a language acquires bilingual information including an original sentence and a translated sentence obtained by machine translation, refers to a terminology database that holds designated words in a language of the original sentence and corresponding designated translation words in a language of the translation sentence in association with each other to generate an instruction that the translated sentence of the bilingual information should be a sentence in which the designated word included in the original sentence of the bilingual information is translated with the designated translation word held in the terminology database in association with the designated word, and acquires a sentence that is the sentence generation processing result obtained using the generation model information as modified translated sentence information.SELECTED DRAWING: Figure 1

A machine translation method includes: obtaining first target language text by performing first translation on source language text using an initial NMT model; identifying an untranslated part in the source language text based on the source language text and the first target language text; obtaining an adjusted NMT model by increasing an attention weight corresponding to the untranslated part in the initial NMT mode; and obtaining second target language text by performing second translation on the source language text using the adjusted NMT model.

The invention provides a video machine translation method and device fusing multi-modal fine-grained information. The multi-modal fine-grained information fused video machine translation method comprises the following steps: performing information extraction on a picture in a video to obtain fine-grained visual information in the picture, and performing information extraction on an audio in the video to obtain fine-grained audio information in the audio; fusing the fine-grained visual information and the fine-grained audio information into a source text to obtain a fused text; the source text is subtitles to be translated in the video; and inputting the fused text into a machine translation model to obtain a target translation text. According to the video machine translation method and device fusing the multi-modal fine-grained information, the fine-grained visual information and the fine-grained audio information of the video are fused into the source text, machine translation is performed based on the fused text, and the translation accuracy is improved.

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 the technical field of scenic spot explanation systems, in particular to an intelligent tour guide robot and AR scenic spot explanation system, and the system comprises a cloud server side which comprises a scenic spot 3D model database, an augmented reality resource package database, a deep learning semantic understanding service module and a multi-language text translation service module; the intelligent tour guide terminal comprises an intelligent tour guide robot, and the robot is provided with a walking mechanism, a voice recognition module, a voice synthesis module, a natural language processing module and a human-computer interaction interface; the AR glasses terminal comprises AR glasses, and the glasses are provided with a camera, a display screen, a processor, a memory and a communication module; through the combination of deep learning semantic understanding and neural machine translation, the accuracy of cross-language communication is improved, and the ability of the system to understand the cultural background is enhanced. The AR technology and the knowledge graph are fused, so that unprecedented knowledge visualization experience is created, and abstract historical and cultural knowledge becomes vivid and perceptible.

The OWS earphone simultaneous interpretation method based on the ASR algorithm comprises the following five steps: S1, generating a voice feature signal after noise reduction through a distributed multi-microphone array; s2, inputting the voice characteristic signal into a dynamic path planning module based on a knowledge graph to generate a multilingual optimization signal; s3, using the optimized signal to control the lightweight hybrid ASR model, and generating a text sequence signal with a path mark through end-cloud cooperative processing; s4, converting the text sequence signal into a target language text through a neural machine translation module; and S5, receiving a user feedback signal through a multi-modal interaction interface, and updating the path weight and the semantic association parameter updating signal in the knowledge graph by using a reinforcement learning mechanism. According to the OWS earphone simultaneous interpretation method based on the ASR algorithm, the problems of low speech recognition precision and high multilingual simultaneous interpretation delay of an open earphone in a complex sound field environment can be solved.

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.

The present invention relates to the technical field of adversarial text processing, and disclosed is a Chinese adversarial sample recovery method based on an HMM and a denoising auto-encoder. The method comprises: acquiring an adversarial text, and carrying out symbol cleaning on the adversarial text; converting pinyin strings in the adversarial text into Chinese character sequences on the basis of a hidden Markov model, and splicing the Chinese character sequences into Chinese character strings; performing denoising on the Chinese characters and the Chinese character strings in the adversarial text by means of a denoising auto-encoder, so as to obtain a candidate word sequence; decoding the candidate word sequence by means of a confidence-similarity decoder, so as to obtain a corrected text; and processing the corrected text by means of bidirectional machine translation to generate a recovered Chinese character text. The present invention can achieve efficient recovery of Chinese adversarial samples.

The invention relates to the field of translation glasses, in particular to a real-time glasses system and device based on neural network machine translation. The technical problem to be solved by the invention is to provide accurate voice acquisition and visual text positioning to ensure translation coherence in a complex scene. The neural network machine translation-based real-time glasses system and device can realize real-time response, multi-language mixed processing and privacy security. The invention discloses a real-time glasses system and device based on neural network machine translation. The real-time glasses system comprises a beam forming microphone array, a micro transparent display screen, a bone conduction loudspeaker, a front camera, an AI processor, an end-to-end neural network processing assembly line, a dynamic context caching module, a user sight tracking module, a privacy protection module and a user interaction module. According to the invention, accurate voice acquisition and visual text positioning are achieved, and translation coherence in a complex scene is ensured; and the effects of real-time response, multi-language mixed processing and privacy security are realized.

Provided is a simultaneous interpretation system that performs machine translation processing and speaker identification processing in real time. In the simultaneous interpretation system, the segment processing unit of the simultaneous interpretation device performs high-speed and highly accurate segment processing to obtain sentence data and also obtains a time range in which a word sequence included in the sentence data was uttered, thus, allowing for performing machine translation processing and speaker identification processing in real time. In other words, in the simultaneous interpretation system, the machine translation processing unit performs machine translation processing on sentence data obtained through high-speed and highly accurate segment processing, and performs, in parallel, processing for predicting a speaker who spoke during the period specified by the time range data based on the inputted video stream and the time range data, thus allowing for performing machine translation processing and speaker identification processing in real time.

A method of facilitating consumption of online content includes receiving source text for a source article to be translated, the source text being in a source language. The source language for the source text and the target language to which the source text is to be translated are each identified. The source text, the source language, and the target language are each provided to a machine translation model which automatically generates translated text in the target language from the source text. The translated text is provided as input to a generative language model which generates summary text in the target language from the translated text. The summary text is provided to a text-to-speech model which generates summary audio from the summary text. The summary text and summary audio are then sent to a user interface via which the summary text is displayed, and playback of the summary audio is enabled.

The invention discloses a multi-language neural machine translation model training method based on meta learning and language pair subnets, and belongs to the technical field of machine translation. The problem that an existing multi-language translation sharing model is poor in translation performance is solved. According to the method, initial parameter optimization is carried out on a multi-language translation model by means of an ML method to obtain a local optimal parameter value, and the ML learns sharing representation among tasks and carries out adaptive adjustment on each language pair, so that parameter interference can be reduced, and translation performance is ensured. A mask matrix of each language to a task can be obtained by learning a language specific subnet, and only model parameters corresponding to input language to data are updated each time during model training, so that a multi-language translation model can avoid parameter interference, and meanwhile, the training efficiency is improved. In the subsequent translation task, only the model parameter corresponding to the element 1 in the mask matrix is needed to participate in translation result prediction, so that the complexity of model calculation is reduced. The method can be applied to the field of machine translation.

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.

A method, computer program product, and computer system are provided for assuring quality of machine translations based on large language models. Data corresponding to an input in a first language is received for machine translation. The received data is translated from the first language to a second language through a translation engine. A confidence value associated with the translated data is determined. The translation is revised with executable source code based on the confidence value being greater than a threshold value. Otherwise, the translation is revised based on sending a prompt to a large language model based on the confidence value being less than the threshold value.

The invention discloses a Mongolian-Chinese neural machine translation method based on constraint enhanced alignment, which comprises the following steps: segmenting Mongolian-Chinese parallel corpora, and preprocessing the segmented corpora; performing random dynamic sampling from the reference translation to obtain Chinese constraints, and splitting Mongolian corresponding to the Chinese constraints into stem and affix forms as Mongolian constraints; adding Chinese and Mongolian constraints to the end of the source sentence to generate a pseudo source sentence, and mixing the pseudo source sentence with the original Mongolian-Chinese parallel corpus to construct a Mongolian-Chinese synthetic parallel corpus; source sentences and constraints in the pseudo source sentences are distinguished through special marks; respectively encoding the source sentence, the Chinese constraint and the Mongolian constraint by using double encoders; adding a word alignment module into the decoder, carrying out decoding operation, training to obtain a final Mongolian-Chinese neural machine translation model, and carrying out Mongolian-Chinese translation; according to the method, the Mongolian-Chinese neural machine translation decoding speed and the proportion of the constraint words appearing in the target sentence can be increased, and the overall translation quality of the Mongolian-Chinese neural machine translation model is improved.

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 present invention provides a communication management system comprising a processor and a non-transitory computer-readable medium with executable instructions. The system securely stores educational content on a blockchain, performs machine translation, generates formatted documents, creates multimedia using GANs, and develops assessments using NLP. It also evaluates content quality via machine learning, issues digital certificates, offers personalized recommendations, facilitates secure transactions, and supports interactive learning experiences through a collaborative interface. The system is accessible across multiple devices and includes a data collector module for extracting and transmitting metadata.

Methods and systems for adapting a pretrained neural machine translation (NMT) model for translating text from a noisy input in a domain. A domain adapter injected into the pretrained NMT model is trained on clean in-domain data for adapting the pretrained NMT to the domain. Each of a plurality of individual noise adapters injected into the pretrained NMT model are trained on noisy input data from an individual noise source for adapting to the individual noise source.

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.

The invention relates to a CAD (computer-aided design) quick translation and typesetting method and system based on a professional knowledge base, and the method comprises the following specific steps: building a self-defined professional lexicon and an API (application program interface) for machine translation, and connecting the API with external machine translation software to form an automatically matched professional knowledge base in a translation process; a user-defined parameter panel is set, selection, translation modes and translation content types of a source language and a target language are included, and the translation modes comprise word and sentence translation and paragraph translation; aiming at the text segment translation mode, splicing multiple lines of characters in the initial text by adopting a coordinate sorting algorithm; setting a self-defined output typesetting format panel which comprises font styles, font sizes, font spacing and the distance between newly-added translated characters and the original text; a professional knowledge base, a self-defined parameter panel and a self-defined output typesetting format panel are integrated in a translation plug-in, and then the translation plug-in is integrated with a CAD system, so that rapid text translation typesetting can be performed by using CAD.

The present invention discloses a machine translation method applied to a stable deep machine translation model. The steps are as follows: during the training process of the deep model, when performing each backpropagation calculation according to the input source language sentence X, two matrices μ and σ are set for the gradient matrix G of the parameters in the encoder part of the deep model; the matrices μ and σ are used to represent the mean and standard deviation of the gradient matrix G respectively, and the values of the matrices μ and σ are calculated based on the gradient matrix G; during each backpropagation process during model training, the gradient matrix G is normalized using the matrices μ and σ; the usage stage and position of this normalization are controlled through a preset strategy and prior knowledge to perform fast and accurate training of the machine translation model, and finally improve the translation quality of the output translation during decoding. The present invention can make the training process more stable, is not limited to specific models and tasks, has good generality, and makes the effects of the model easier to reproduce on different devices.

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 invention provides a translation method and system based on a semantic energy conservation network, a medium, equipment and a program product, and belongs to the technical field of machine translation. In the technical scheme, vocabularies of input source language sentences are expressed in the form of energy, from the perspective of energy conservation, a Hamiltonian deep neural network HDNN and a VI Verlet integrator are used as an energy evolution network for processing evolution of vocabulary position and active quantity information, the relative position and the active quantity after evolution are input into a Transform-based translation network, and a translation result is obtained. The total Hamiltonian energy of the sentences is recalculated through the energy evaluation network, and then the energy difference is calculated to evaluate the translation loss. According to the method, the idea of Hamiltonian dynamics and the advantages of deep learning are combined, evolution is carried out through semantic energy conservation, vocabulary information evolution and feature extraction are carried out through HDNN, the stability and consistency of information are guaranteed, the model can better understand the context relation of an input text, and translation accuracy can be improved.

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 present disclosure relates to a method and device for machine translation of a text, and a storage medium. The method includes: identifying entity words in a text; replacing the original sentence in the text with a placeholder for the entity word to obtain a replacement sentence containing the placeholder, wherein the placeholders for different entity words are different; inputting the replacement sentence and the entity word extracted from the original sentence into a translation model respectively to obtain a first translation result of the replacement sentence and a second translation result of the entity word; combining the first translation result and the second translation result to obtain a translation result of the original sentence. Through this method, the consistency of entity word translation in a text can be improved, and the user experience can be improved.