16 results about "Natural language translation" patented technology

The invention provides an intelligent mechanical arm control system scheme based on multi-model cooperation, and belongs to the field of automation and intelligent control. The system is divided into four parts of man-machine interaction, detection and perception, task decoupling planning and program generation, and robot execution operation. Man-machine interaction is carried out, a user inputs an instruction in a text or voice format, and instruction processing is carried out through voice recognition and a natural language translation model; detecting and sensing, and identifying and positioning a target captured by the camera by using a multi-modal large model; task decoupling planning and program generation: analyzing the user instruction and the perception information into an executable program by applying a task planning model and a program translation model; and checking and correcting the program by using the program checking model, and driving the mechanical arm to complete a task. According to the system scheme, variable environments can be dealt with, complex language instructions can be understood, intelligent control over the mechanical arm is achieved, and the multi-task execution capacity and the intelligent level of the mechanical arm are remarkably improved.

The invention provides a sign language translation method and system based on a pre-training diffusion large language model, and belongs to the field of sign language video translation. The method comprises the following steps: preprocessing a video containing sign language actions to obtain a sign language video frame sequence, inputting the sign language video frame sequence into a visual feature extraction network to extract features, and fusing to obtain a time sequence visual fusion feature sequence; giving a text cue word of a sign language translation task, constructing an initial mask sequence for a target translation position, taking the text cue word, the time sequence visual fusion feature sequence and the initial mask sequence as guide conditions, injecting the guide conditions into a diffusion language model, iteratively denoising and predicting lexical elements of a masked position in combination with a diffusion mask mechanism, and obtaining the sign language translation task. A natural language translation sequence is obtained, and sign language translation is completed; wherein when the diffusion language model is trained, through an internal feature alignment mechanism, the guiding effect of guiding conditions on text generation is optimized, so that the accuracy, coherence and robustness of long text translation are improved, and the actual requirements of a barrier-free public service scene are better met.

A computerized method is provided for using large language models to integrate data from multiple sources including technical metadata (e.g. column and table names) with a dictionary of standard abbreviations employed in the metadata, representative data from the columns rows of the table, a business glossary of terms in the data, and representative queries used to interrogate the data, to create a non-technical or natural language description of business value of the table and its columns and data.

The invention relates to the technical field of network deployment, in particular to a method and system for interactively configuring parameters through an LLM and a router gateway. The method comprises the following steps: arranging corresponding data of a natural language, a router command and a command execution result, and forming Quest / Answer training data required by a large model for providing a semantic mapping basis of a router configuration field for the large model; and training the general model through a large model fine tuning technology, so that the general model translates a router configuration related natural language into a specific router command and translates a command execution result into a natural language. According to the method, corresponding data pairs of a natural language, a router command and an execution result are constructed, and targeted fine adjustment is performed on a large model, so that the model has a bidirectional semantic translation capability in the field of router configuration, and the technical bottleneck that a traditional interaction scheme depends on independent interface development is broken through.

A method of translating software queries into natural language may comprise receiving a user instruction to identify a data set through a database query defined by a received user-selected query object and user-selected query value in setting a portion of a currently modeled integration process, and to perform a user-selected action on the data set, and translating a suggested database query associated in memory with the user-selected query object to a natural language translation. The method may also comprise displaying the natural language translation of the suggested database query, receiving a user instruction to include the suggested database query in the currently modeled integration process, and automatically generating and transmitting to a remote location for later execution, code instructions for performing the user-selected action on data sets stored at the remote database meeting the selected, suggested database query.

The disclosed computer-implemented method for performing natural language translation in AR may include accessing an audio input stream that includes words spoken by a user in a first language. The method may next include performing active noise cancellation on the words in the audio input stream such that the spoken words are suppressed before reaching the listening user. Additionally, the method may include processing the audio input stream to identify the words spoken by the user and translating the identified words spoken by the user into a different second language. The method may further include generating spoken words in the different second language using the translated words and playing back the generated spoken words in the second language to the listening user. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed technology generally relates to malicious code detection using semantic understanding. In one example of the technique, a natural language output is generated by performing a natural language translation of code. A machine learning model is used to generate a confidence score indicating a confidence that the natural language output is potentially malicious. Based at least in part on whether the confidence score exceeds a first threshold, a determination is made as to whether the natural language output is potentially malicious. In response to determining that the natural language output is potentially malicious, the code is marked as potentially malicious.

A computer-implemented method for training a natural language translation model. The computer-implemented method includes: processing one or more sets of electronic parallel documents to obtain a plurality of aligned parallel sentences; creating a first training set including a subset of the plurality of aligned parallel sentences; and training the natural language translation model in a first stage using the first training set. The computer-implemented method further includes: modifying the first training set based on translation errors detected after the first stage of training; creating a second training set based on the modified first training set and at least some of the plurality of aligned parallel sentences not in the first training set; and training the natural language translation model in a second stage using the second training set so as to improve translation performance of the natural language translation model.

The application relates to the technical field of natural language translation, in particular to a cross-language intelligent translation optimization method and system based on a proper name library, which comprises the following steps: for text data to be translated, performing paragraph segmentation and word segmentation, and obtaining all words and semantic feature vectors in each paragraph; matching the words in each paragraph with a plurality of predefined disambiguation libraries, screening out candidate special words of each paragraph, and determining the first matching degree of each candidate special word in each paragraph; calculating the second matching degree and matching coefficient of each candidate special word and each disambiguation library, and screening the disambiguation library according to the second matching degree and the matching coefficient to translate the candidate special words. The application can more comprehensively and stably evaluate the real field attribution of the candidate special words in a specific context, and significantly improve the accuracy and professionalism of translation.

Systems, computer program products, and methods are described herein for automated translation of digital multimedia. The present disclosure is configured to receive, from a user input device, a search request; determine the response for the search query in a first natural language, wherein the response comprises digital multimedia; reconfigure the response for display on the user input device in the preferred natural language, wherein reconfiguring further comprises: determining an intermediate natural language based on a lexical similarity with the preferred natural language; translating, using a first natural language translation subsystem, the response from the first natural language to the intermediate language; and translating, using a second natural language translation subsystem, the response from the intermediate language to the preferred natural language; and display the reconfigured response on the user input device in the preferred natural language.

The application provides a sign language translation method and system based on a pre-training diffusion large language model, and belongs to the field of sign language video translation. The video containing sign language actions is preprocessed to obtain a sign language video frame sequence, which is then input into a visual feature extraction network to extract features and fuse to obtain a time sequence visual fusion feature sequence; a text prompt word of a sign language translation task is given, and an initial mask sequence is constructed for a target translation position; the text prompt word, the time sequence visual fusion feature sequence and the initial mask sequence are injected into a diffusion language model as a guide condition, and a diffusion mask mechanism is combined to iteratively denoise and predict the word units of the masked position to obtain a natural language translation sequence, thereby completing the sign language translation; during training of the diffusion language model, an internal feature alignment mechanism is used to optimize the guiding effect of the guide condition on text generation, so as to improve the accuracy, coherence and robustness of long text translation and better meet the actual needs of barrier-free public service scenarios.

Systems, computer program products, and methods are described herein for automated translation of digital multimedia. The present disclosure is configured to receive, from a user input device, a search request; determine the response for the search query in a first natural language, wherein the response comprises digital multimedia; reconfigure the response for display on the user input device in the preferred natural language, wherein reconfiguring further comprises: determining an intermediate natural language based on a lexical similarity with the preferred natural language; translating, using a first natural language translation subsystem, the response from the first natural language to the intermediate language; and translating, using a second natural language translation subsystem, the response from the intermediate language to the preferred natural language; and display the reconfigured response on the user input device in the preferred natural language.

Techniques for automatically generating a natural language (NL) translation of computer code are disclosed. In one embodiment, a computer-implemented method is disclosed comprising receiving, from a user, a code translation request in connection with code generated by a code generation system based on natural language (NL) input, analyzing the computer-generated code and generating a natural language (NL) translation of the computer-generated code based on the analysis, generating a graphical user interface (GUI) comprising the NL input, the NL translation of the computer-generated code and GUI control elements for receiving input from the user in connection with at least one of the NL input and the NL translation; causing the GUI to be displayed at a client device of the user, and receiving input from the user via at least one GUI control element and causing performance of at least one operation in response to the input.

The present disclosure relates to a model training method, a natural language translation method, a device, equipment and a storage medium. The present disclosure obtains a standard first sign language text corresponding to a natural language sample text and at least one non-standard second sign language text, and outputs a predicted sign language text corresponding to the natural language sample text through a translation model to be trained. According to the predicted sign language text and the first sign language text, a first loss value of the translation model is calculated, and according to the predicted sign language text and at least one second sign language text, at least one second loss value of the translation model is calculated. Since the smaller the first loss value is, the better, and the larger the second loss value is, the better, according to the first loss value and the at least one second loss value, the model parameters of the translation model can be accurately controlled, so that the trained translation model is more accurate. Thus, the trained translation model can obtain accurate sign language text when translating the natural language text to be translated.

The invention discloses an intelligent building design and construction method and system based on AI and CAD. The method comprises the steps of semantic CAD modeling, knowledge graph reasoning, emotion AI evaluation, agent behavior simulation, law and regulation constraint embedding, natural language translation, multi-objective optimization and the like, and full-automatic generation from natural language requirements to compliance constructable design schemes is achieved. The system can output a design decision chain, a standard verification report and an emotion evaluation result, obviously improves the intelligence and interpretability of building design, and has high efficiency and innovativeness.

Techniques are disclosed herein for calibrating confidence scores of a machine learning model trained to translate natural language to a meaning representation language. The techniques include obtaining one or more raw beam scores generated from one or more beam levels of a decoder of a machine learning model trained to translate natural language to a logical form, where each of the one or more raw beam scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more beam levels, classifying, by a calibration model, a logical form output by the machine learning model as correct or incorrect based on the one or more raw beam scores, and providing the logical form with a confidence score that is determined based on the classifying of the logical form.