686 results about "First language" patented technology

One example method includes selecting an entry from a validation set based on similarity to a first communication record, wherein the entry from the validation set includes a second communication record and a corresponding summary of the second communication record; generating a prompt that includes the first communication record, the second communication record, and the corresponding summary of the second communication record; inputting the prompt to a first language model to obtain a generated summary of the first communication record; and training a second language model based on the first communication record and the generated summary.

The invention provides a cue word optimization method and device, equipment, a medium and a product, and the method comprises the steps: obtaining at least one first cue word related to a target business, and obtaining a business data sample set related to the target business; for each first cue word in the at least one first cue word, executing the following steps: acquiring a first evaluation result of the first cue word on a business data sample set, and determining error sample information corresponding to the first cue word based on the first evaluation result; sending a target cue word used for optimizing the first cue word to a first language model; and obtaining a second cue word output by the first language model. In the method, aiming at the cue word to be optimized, the task processing effect of the cue word in a business data sample set is evaluated, the cue word is optimized by combining related information of an error data sample and utilizing a language model, the cue capability of the optimized cue word in a target business is improved, and the cue effect of the cue word is optimized.

The invention provides a multi-mode voice interaction method based on a large model, electronic equipment and a storage medium, and the method comprises the steps: carrying out the voice recognition of the voice data of a first language inputted by a user, and obtaining a first language text; performing language translation on the first language text to translate the first language text into a second language text; performing semantic analysis on the second language text to obtain a semantic analysis result; performing multi-modal knowledge graph construction on the semantic analysis result based on a preset large model to obtain an enhanced semantic understanding result; wherein the multi-modal information related to the second language text is fused in the multi-modal knowledge graph; generating a dialogue strategy of the enhanced semantic understanding result; and performing voice synthesis based on the dialogue strategy to generate reply voice data expressed in the first language. According to the method and the device, multi-language interaction is realized, and meanwhile, various modal information is fused.

Disclosed are apparatuses, systems, and techniques that may use machine learning for reversible translations of speech utterances. The techniques include training and using duplex neural networks (NNs) having a first subnetwork and a second subnetwork that are mirror images of each other. Training data for training the duplex NNs may include a target output that includes a first speech utterance in a first language, a first training input that includes the target output distorted by a noise, and a second training input that includes a second speech utterance in a second language. The duplex NNs may be trained to identify, using the first training input and the second training input, at least one of the target output or the first noise.

Techniques for selecting a skill to execute in response to a natural language input are described. A system may receive a natural language input, determine profile data associated with the natural language input, and determine the profile data indicates a locale and at least first language and second languages. The system determines first and second sets of skills corresponding to the locale / first language and locale / second language, respectively. The system determines a first group of skill candidates corresponding to a portion of the first set of skills, and determines a second group of skill candidates corresponding to a portion of the second set of skills. The system performs ranking processing across the first and second groups of skills to determine a best skill for responding to the natural language input. Thus, in some situations, the skill invoked may not correspond to the same language represented in the natural language input.

Sanitizing data can be a cumbersome task, particularly when the volume of data is large, the content is sensitive, and / or the type of sanitation requires contextual determinations. Sanitizing large amounts of data is tedious and may often require highly trained personnel with clearances and / or other qualifications. In the systems and methods of the present disclosure, language models (LMs) are used to solve these and other technical issues with tools that may allow sanitizing data easily, with high versatility, context awareness, and / or low demand for computational resources. In particular, some of the disclosed systems and methods use a first language model and a second language model (being less resource-intensive than the first language model) to generate sanitized output data with improved efficiency and accuracy. This dual-model approach ensures that sensitive information is handled appropriately while optimizing computer resource usage.

Systems and methods for generating speaker video and audio in multiple languages for videoconferencing are provided. For example, a computing device can access a speaker speech audio signal that includes a speaker speech in a first language, a video of the speaker and a translated speech audio signal of the speaker speech in a second language. The computing device generates, based on the translated speech audio signal, a converted translated speech audio signal that includes a speech in the second language having voice characteristics in the speaker speech. The computing device further generates a lip-synched speaker video based on the video of the speaker and the converted translated speech audio signal. Lip movements in the lip-synched speaker video correspond to the converted translated speech audio signal. The converted translated speech audio signal and the lip-synched speaker video are transmitted to a video conference provider configured to host the video conference.

The invention discloses an automatic multi-language construction method, system and device and a storage medium, which are used for improving the generation efficiency of a language package and reducing the maintenance cost of multi-language development. The method comprises the following steps: acquiring a project source file, and constructing a tree structure file based on the project source file; traversing the tree structure file, and identifying and extracting text content in the tree structure file; distributing a corresponding position label for each text content, and generating a first language package based on the text content and the position label; extracting a to-be-translated target text in the first language package, and obtaining translation results of a plurality of preset languages in a translation result library according to the target text; generating a second language package of a plurality of preset languages according to the translation result and the position tag; when a request of switching to a target language is received, determining a target language package corresponding to the target language in the second language package; and performing text replacement on the current text content by using the target language package and the target position tag corresponding to the current text content.

A video content item includes a video portion and an audio portion to be translated to a first language from a second language. The audio portion in the first language is caused to be visually correlated with the video portion in the second language. Causing the audio portion in the first language to be visually correlated with the video portion in the second language comprises providing translation content representing the audio portion in the first language as input to a machine learning model, generating, using an output of the machine learning model, one or more variations of the audio portion in the first language, and selecting, using a comparison of a speech component of an audio portion in the second language to a speech component of each of the audio portion in the first language and the one or more variations of the audio portion in the first language.

The present invention provides a data extraction method for entity evaluation. The method is used for extracting target data on the basis of a predefined indicator, and comprises: an underlying data processing step: at least comprising using a first language model to identify identical events in underlying data; and an indicator data extraction step: at least comprising, on the basis of the predefined indicator, using the first language model to extract target data from among the underlying data, wherein a base large model of the first language model comprises a large language model based on a Transformer model architecture, and the first language model is obtained by training on the basis of the base large model. The technology of the present invention can significantly improve the accuracy of data extraction and reduce training costs.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing text-to-speech modeling. In some implementations, a computer device receives input text in a first language to convert to a desired speech in a second language. The computer device receives one or more criteria for modifying the desired speech and converts the input text to a desired text in the second language. The computer device generates audio representations of the desired text in the second language and predicts, for each of the audio representations of the desired text and using the one or more received criteria, a pitch value and a duration value. The computer device generates the desired output speech using the predicted pitch value and the predicted duration value for each of the audio representations and provides the desired speech for output.

A method for generating a first case dataset in a first language. The method includes receiving adverse event data. The method further includes determining case data including general case data and regional case data and providing the case data to a translator computing device to enable display on a user interface including multiple duolingual text fields with a first language text field including at least a portion of the text data in the first language and a second language text field adjacent the first language text field. The method further includes receiving the text data in the second language from a translator computing device. The text data in the second language is received via the second language text fields of the plurality of duolingual text fields. The method further includes generating and outputting the first case dataset including the text data in the first language.

Provided herein are systems, methods, and computer-readable media for improving results from machine learning models. An example method may include obtaining a user input query; generating first logits from a first language model by applying the first language model to the user input query; generating second logits from a second language model by applying the second language model to the user input query; combining the first logits and the second logits; determining probabilities associated with tokens from the combined first logits and second logits; and generating an output token based on the determined one or more probabilities.

Methods, apparatus, and processor-readable storage media for filtering content for automated user interactions using language models are provided herein. An example method includes obtaining a plurality of portions of content based on a query corresponding to one or more topics related to an organization, where the plurality of portions of content is retrieved from at least one content source corresponding to the organization, and configuring a first language model instance to generate a score for each portion of content in the plurality of portions of content based on its relevancy to the query. The method includes filtering the plurality of portions of content based at least in part on one or more filtering criteria and the score generated for each portion, and generating, using a second language model instance, a response to the query, where the response is based on the portions of content resulting from the filtering.

A method includes receiving an input text sequence to be synthesized into speech in a first language and obtaining a speaker embedding, the speaker embedding specifying specific voice characteristics of a target speaker for synthesizing the input text sequence into speech that clones a voice of the target speaker. The target speaker includes a native speaker of a second language different than the first language. The method also includes generating, using a text-to-speech (TTS) model, an output audio feature representation of the input text by processing the input text sequence and the speaker embedding. The output audio feature representation includes the voice characteristics of the target speaker specified by the speaker embedding.

Devices and techniques are generally described for unlearning information from large language models (LLMs). In various examples, a first language model (LM) trained on a first training corpus D may be determined. First data F that is a subset of D may be determined. A first auxiliary LM may be trained using the first training corpus D and a second auxiliary LM may be trained using a second training corpus D / F, where the second training corpus D / F represents the first training corpus D without the first data F. A first text input may be determined. The first LM may be updated based at least in part on a first prediction difference between predictions the first LM and the second auxiliary LM for a first set of inputs and a second prediction difference between the predictions of the first LM and the first auxiliary LM for the first set of inputs.

The invention relates to a training method of a translation model, a text translation method and a training method of a generative model. The method comprises the steps that a training text of a first language is input into an initial translation model, a model output text is obtained, and the model output text comprises a model translation text of a second language; obtaining a format reward corresponding to the model output text based on the format of the model output text, wherein the format reward is used for representing whether the model output text accords with a preset format; obtaining a measurement reward corresponding to the model translation text based on the training text and the model translation text, wherein the measurement reward is used for representing the translation quality of the model translation text; obtaining a target reward according to the format reward and the measurement reward; and training the initial translation model based on the target reward and a preset reinforcement learning algorithm to obtain a target translation model. According to the training method, a format and measurement mixed reward mode is adopted, a guidance signal with rich and effective information is provided for reinforcement learning, and the translation quality of the translation model can be improved.

A method for extracting and processing audio data may include receiving one or more packets of multimedia content. The one or more packets of multimedia content may comprise audio data. The method may further include extracting the audio data from the one or more packets of multimedia content. The audio data may comprise verbal speech in a first language. The method may further include converting the audio data into first text data in the first language based on the verbal speech in the first language. The method may further include providing the first text data to a generative machine-learning model. The generative machine-learning model may have been trained to translate the first text data in the first language to a second language and generate second text data in the second language. The method may further include transmitting, to a user interface, the second text data in the second language.

A method for generating a first case dataset in a first language. The method includes receiving adverse event data. The method further includes determining case data including general case data and regional case data and providing the case data to a translator computing device to enable display on a user interface including multiple duolingual text fields with a first language text field including at least a portion of the text data in the first language and a second language text field adjacent the first language text field. The method further includes receiving the text data in the second language from a translator computing device. The text data in the second language is received via the second language text fields of the plurality of duolingual text fields. The method further includes generating and outputting the first case dataset including the text data in the first language.

Embodiments of the present disclosure disclose a text generation method and apparatus, an electronic device, and a storage medium. The method includes: extracting events from a video to be processed, and determining target video frames corresponding to the events; extracting frame features of the target video frames, and determining event features based on the frame features; concatenating the event features in an extraction order of corresponding events, to generate a prompt text; and generating, by a first language model, a description text of the video to be processed based on the prompt text. By concatenating the event features in the extraction order of the corresponding events to obtain the prompt text, and inputting the prompt text into the first language model, the first language model can be enabled to perceive content and order of different events in the video, which can improve the text generation effect.

The invention discloses a language migration detection method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps of obtaining a first language source code and a second language migration code, wherein the second language migration code is obtained by migrating the first language source code to a second language environment; analyzing the first language source code to obtain a first abstract syntax tree, and analyzing the second language migration code to obtain a second abstract syntax tree; respectively encoding each first node of the first abstract syntax tree and each second node of the second abstract syntax tree through a pre-trained semantic encoder to obtain a first semantic vector of each first node and a second semantic vector of each second node; and inputting the first semantic vector and the second semantic vector into a pre-trained logic detection model, and determining whether the logic of the first language source code and the logic of the second language migration code are consistent or not through the logic detection model, thereby realizing comprehensive detection of the business logic of the source code and the migration code.

The invention provides an information processing method and device, a model training method and device, equipment, a medium and a product, and relates to the technical field of computers. The public opinion information processing method comprises the steps that on the basis of a first language model, a crawled original language text is coded to obtain an original language vector, on the basis of a second language model, a target language text is coded to obtain a target language vector, the target language text is generated by translating the original language text, and the first language model is a multi-language model; generating a sentence-level first fusion vector and a word-level second fusion vector based on the original language vector and the target language vector; obtaining corresponding theme features and emotion features based on the first fusion vector; performing entity extraction on the second fusion vector to obtain entity features; and generating a public opinion analysis result based on the subject features, the emotion features and the entity features. According to the technical scheme, the public opinion association capture capability is enhanced through multi-feature joint analysis, and the public opinion analysis efficiency and accuracy can be improved.

An AI-based application uses AI models to expeditiously identify a product or service most relevant to an issue input to the application instead of a user navigating a platform or catalogue of products / services. The application prompts a first language model to convert the input description into a structured description of the issue. The application then, for each product / service category, prompts a second language model to identify a product / service most relevant to the structured description within each product / service category. The application then returns an identification of a product / service determined to be most relevant to the issue as represented by the structured description, confidence in the identification, and a reason the product / service was identified.

An autonomous agent receives a user query comprising the complex question. The agent can obtain, from a corpus of unstructured texts, an answer candidate text corresponding to the user query and comprising text from which the answer is subsequently identified. The agent may generate first linguistic data corresponding to the user query and second linguistic data corresponding to the answer candidate text. Each instance of linguistic data may comprise a combination of respective syntactic data, semantic data, and discourse data generated from the user query and / or answer candidate text. Both instances of linguistic data may be provided to a machine-learning model that has been previously trained to output an answer identified from an instance of unstructured text (e.g., the answer candidate text). The model may output the answer identified from the answer candidate text, which in turn may be provided in response to the user query.

The invention provides a behavior learning-oriented intelligent operation model construction method, device and system, and relates to the technical field of data processing, and the method comprises the steps: generating a sample user operation information sequence used for achieving a sample task in target equipment based on an operation image and an operation log; wherein sample user operation information in the sample user operation information sequence comprises an operation instruction determined based on the operation log and an operation target determined based on the operation image; inputting sample natural language instruction information corresponding to the sample task into a first language model to obtain a first operation information sequence output by the first language model; and based on a loss value between a first operation information sequence corresponding to the sample task and a sample user operation information sequence of the sample task, adjusting model parameters of the first language model to obtain an intelligent operation model. The efficiency of obtaining task implementation steps and the efficiency of executing a target task by a user through target equipment can be improved.

The invention relates to a speech translation method, device and equipment of an end cloud translation system and a medium, and relates to the technical field of intelligent speech translations, the end cloud translation system comprises an audio receiving end and a cloud, the method is applied to the audio receiving end, and the method comprises the following steps: obtaining a first language audio of the audio receiving end; sending the first language audio to a cloud; and receiving and playing a second language audio, wherein the second language audio is translated by the cloud according to the first language audio and then is sent out. The method can be widely applied to cross-border conference scenes, and the cross-language communication efficiency and experience are improved.

The invention discloses a federal large model fine tuning method and device based on mutual learning and a medium, and relates to the technical field of large language models. The method comprises the following steps: finely adjusting a first language model through a client and a local private data set to obtain a second language model; calculating the public data set through a second language model to obtain a second result; calculating a first loss value of the second result and the first result, and forming a first knowledge set by the second result and the first loss value; obtaining a knowledge set, and calculating the public data set through a third language model to obtain a third result; a second loss value of the third result is calculated, and the first loss value and the second loss value are compared to fine tune the third language model. According to the method, the knowledge of the small language model of the client side is fully utilized to enrich the large language model of the server side, and meanwhile the knowledge of the large model of the server side is used for improving the capacity of the small language model of the client side.

A method of enabling a language model trained in a first language to support a second language includes extending an existing vocabulary of the language model to include additional tokens for text in the second language. The method also includes initializing the additional tokens for text in the second language based on subtokens of tokens for text in the first language from the existing vocabulary. The method further includes training the language model using a mixed language dataset that includes a first language corpus and a second language corpus. In addition, the method includes performing instruction tuning using a dataset that includes (i) instruction and response pairs involving the first language and (ii) instruction and response pairs involving the second language.

To provide high-precision dialogues while suppressing costs.SOLUTION: A processing device: recognizes contents of a user's utterance; generates a prompt text including a list of multiple first language models specialized for understanding intent in multiple predetermined domains and the contents of the utterance; inputs the prompt text into a second language model; identify the first language model with a domain corresponding to the content of the utterance based on the contents of the response obtained from the second language model; and transfers the contents of the utterance to the identified first language model.SELECTED DRAWING: Figure 1

The present disclosure relates to a method, a device, and a computer program product for generating video. The method includes extracting a first text of a first language in an input video and generating a second text of a second language corresponding to the first text of the first language. The method further includes generating a gist frame of the input video based on the input video and the second text, where the gist frame indicates information associated with color configuration and object layout of the input video. In addition, the method further includes generating, based on the gist frame and the second text, an output video broadcasting the second text by means of the second language. The method of the present disclosure enhances the similarity of the output video and the input video, and expressions and actions of a narrator can be generated at a correct time point.