44 results about "Paraphrase" patented technology

The invention belongs to the field of natural language processing, and discloses a large language model illusion detection method and system based on paraphrase recognition, and the method comprises the steps: obtaining generated text content according to an initial question through a to-be-detected large language model, and obtaining a plurality of inference questions according to the generated text content through a preset large language model; obtaining the vector representation of the initial problem and each inference problem through a preset comparison learning model; obtaining a consistency score between the vector representation of the initial question and the vector representation of each inference question through a preset illusion detection model, and aggregating the consistency score to obtain a comprehensive score; and obtaining an illusion test result of the to-be-detected large language model according to the comprehensive score and a preset consistency threshold. A strategy based on reverse thinking is innovatively provided, dependence on external knowledge is reduced, detection variability and identifiability are improved, technical complexity is greatly reduced, factual illusion and loyalty illusion are detected at the same time, and accuracy of illusion detection is greatly improved.

Techniques are described for a machine learning system configured to generate respective sample embeddings for a plurality of sample statements. The machine learning system may further be configured to generate a statement embedding for a statement. The machine learning system may further be configured to determine, based on the sample embedding and the statement embedding, respective similarity scores for the sample embeddings. The machine learning system may further be configured to select, based on the respective similarity scores for the sample embeddings, one or more sample statements from the plurality of sample statements. The machine learning system may further be configured to generate a prompt including the one or more sample statements, the statement, and at least one of respective ground-truth information or respective paraphrases for the selected one or more sample statements. The machine learning system may further be configured to provide the prompt to a machine learning model.

A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include generating training data for an intent classification machine learning model by: (a) determining, via a text-to-text machine learning model, one or more respective paraphrases for each sample phrase of training phrases; (b) generating, via a label generating machine learning model, labeled data based on unlabeled live logs by: (i) determining live-log samples from the unlabeled live logs based at least in part on: a respective timestamp of each live log of the unlabeled live logs, or random sampling; and (ii) generating, via the label generating machine learning model, the labeled data based on the live-log samples and one or more labeling functions; and (c) adding the one or more respective paraphrases for the each sample phrase of the training phrases and the labeled data to the training data. In certain embodiments, a respective quantity of the one or more respective paraphrases can vary for the each sample phrase of the training phrases. In some embodiments, the method further can include transmitting the training data, as generated, to the intent classification machine learning model for training. Other embodiments are described.

The invention discloses a Chinese network violent event data set construction method based on man-machine collaboration, which comprises the following steps: 1) extracting comment data related to network violent events from a plurality of Chinese social media platforms, and ensuring data diversity and complexity; 2) adopting three network violence detection methods based on paraphrasing, thinking chain and multiple agents to generate pseudo labels and corresponding interpretation contents, and combining results of the three detection methods through an integration method; 3) performing manual labeling by a plurality of Chinese native language persons according to the generated pseudo labels and explanations to ensure the labeling accuracy; and 4) constructing an event-based Chinese network violent detection data set CHNCI according to a labeling result, and carrying out statistics on basic information of the data set. According to the method, the Chinese network violent detection data set based on the events is constructed in a mode of combining machine generation of pseudo labels and manual annotation, the data annotation cost is remarkably reduced, and meanwhile the coverage range and quality of the data set are improved.

The invention provides an intention recognition method and device, electronic equipment, a storage medium and a program product, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining a target intention of a user query statement based on a first intention classification result of the user query statement and a second intention classification result of a statement paraphrase corresponding to the user query statement; the statement paraphrasing is output by the statement paraphrasing model after the user query statement is input into the statement paraphrasing model. When intention recognition is carried out on the user query statement, paraphrase processing is additionally carried out on the user query statement to obtain the corresponding statement paraphrase, multi-layer semantics input by the user are mined, and meanwhile the final target intention of the user query statement is jointly determined by considering the user query statement and the intention classification result of the statement paraphrase of the user query statement. The intention recognition accuracy can be improved when scenes such as complex query, multi-intention problems and deep semantic understanding are processed, and finally the accuracy of subsequent intention recognition such as intelligent questions and answers and the user experience degree are improved.

The application belongs to the field of natural language processing, and particularly relates to a method and system for paraphrase recognition based on semantic primitive knowledge and abstract semantic representation, which comprises the following steps: performing word segmentation on a sentence, and performing word-level vector representation and semantic primitive knowledge representation; performing mean value processing on the semantic primitive knowledge representation result, and extracting interactive attention feature information of the mean value processing result by using global semantic information to obtain global semantic primitive representation; performing abstract semantic analysis on a to-be-recognized paraphrase sentence from a sentence structure to obtain a single-root directed acyclic graph, and performing global semantic primitive representation and word-level vector representation; extracting global and local feature information in the order of the directed acyclic graph, and performing distance feature measurement on the information; inputting the distance feature measurement result into a neural network to obtain a recognition result; the application introduces external semantic primitive knowledge to perform semantic representation, the accuracy of the semantic primitive knowledge representation is assisted by global semantic information, and the abstract semantics of a Chinese paraphrase sentence is analyzed to obtain semantic relations, so that the accuracy of paraphrase recognition is improved.

The invention discloses a data visualization narrative generation method, device and equipment based on paramagnetism, and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: generating sentence-level parastyle features according to parastyle contents input by a user; generating corresponding plot information for the parastyle features; according to data input by a user, determining data feature information corresponding to the data feature name in the external features; generating visual structure information of features according to the data feature information; generating animation structure information of the parastyle features according to the parastyle features and the plot information, the data feature information and the visual structure information corresponding to the parastyle features; and obtaining to-be-rendered data based on the white features and the plot information, the data feature information, the visualization structure information and the animation structure information corresponding to the white features, and rendering the to-be-rendered data to generate a data visualization narrative picture. The visual narrative content with high personalization is generated according to the generation of the visual narrative driven by the user-defined parastaring.

The invention discloses a word reciting auxiliary device for English education, and belongs to the technical field of teaching equipment. The through grooves are formed in the writing board and correspond to one another in pairs; the rotating box is rotationally connected into the through groove, and the front side of the rotating box is open; every two writing cards are arranged in pairs, the writing cards are inserted into the rotating box, and the writing surfaces of the writing cards face the open surface of the rotating box; a writing pen. Words and paraphrases of the words are written on the writing cards to memorize the words once, then the paired writing cards are placed in the corresponding rotating boxes to memorize the words and the corresponding paraphrases once, the paraphrases corresponding to the words or the words corresponding to the paraphrases are written from memory by turning over the rotating boxes, and the words and the paraphrases of the words are memorized again. According to the method, the words are repeatedly memorized for multiple times, and the word memorizing modes and methods are different, so that the interestingness of word memorizing is increased, and meanwhile, the word memorizing efficiency and effect are improved.

Systems and techniques that facilitate ground-truth-less performance prediction of generative question-answering systems are provided. In various embodiments, a system can access a large language model (LLM) and a natural language question for which a ground-truth answer is unavailable. In various aspects, the system can generate, via a machine learning classifier that receives as input a set of properties associated with the natural language question, a classification label indicating whether or not the large language model will correctly answer the natural language question. In various instances, the set of properties can include a semantic category of the natural language question, a subject popularity of the natural language question, a semantic consistency exhibited by the LLM in response to repeated executions on the natural language question, or a semantic consistency exhibited by the LLM in response to execution on paraphrases of the natural language question.

The invention discloses a pictographic phrase paraphrase mining method and device, equipment, a medium and a product. The method for mining the phrase paraphrases of the pictographs comprises the following steps: determining inter-word probability distribution of the pictographs in a corpus through a Transform model, and determining candidate phrases according to the inter-word probability distribution; for each candidate word group, determining an image segment corresponding to the candidate word group and a single sentence paraphrase text corresponding to the candidate word group; and through an image-text alignment model, determining segmented words corresponding to the candidate phrases in the single sentence paraphrase text to obtain paraphrases corresponding to the candidate phrases. According to the technical scheme, the candidate phrases are determined according to the inter-word probability distribution, the segmented words corresponding to the candidate phrases are matched from the corresponding single sentence paraphrase text through the image-text alignment model, the paraphrases of the candidate phrases are obtained, and effective and accurate mining of the paraphrases of the pictographs is achieved.

The invention relates to the technical field of vocabulary memory training, and discloses a vocabulary memory training method and system based on handwriting interaction, and the system comprises a dictation module which is configured to enter a handwriting training environment after determining a to-be-memorized vocabulary, play the voice of the to-be-memorized vocabulary and display the paraphrase of the to-be-memorized vocabulary, and then carry out the handwriting input of a user; the acquisition module is configured to determine a next-round initial training interval based on the analysis result; the judging module is configured to judge whether to adjust the initial training interval of the next round according to the writing characteristic parameters; the processing module is configured to determine an adjustment coefficient of a next-round initial training interval based on the historical training parameters and obtain a next-round final training interval; and the execution module is configured to train the vocabularies to be memorized at the following final training interval. According to the method, the next training interval can be scientifically and reasonably determined according to the handwriting input result of the user, the writing characteristic parameters and the historical training parameters of the vocabularies to be memorized, and personalized vocabulary memorizing training is achieved.

A computing system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to: receive a paraphrase comprising a paraphrase text sample corresponding to an original text sample; and calculate a paraphrase metric value corresponding to the paraphrase, wherein the paraphrase metric value is calculated based on an adequacy score, a novelty score, and a fluency score of the paraphrase, the adequacy score indicating an extent to which the paraphrase text sample preserves a meaning of the original text sample, the novelty score indicating a level of difference between words and characters of the paraphrase text sample and words and characters of the original text sample, and the fluency score indicating an extent to which the paraphrase text sample is devoid of repetition, spelling, and grammatical mistakes.

The invention discloses a book reading method and system based on artificial intelligence, a medium and a product, and relates to the field of data processing. In the method, a book text is segmented into a plurality of text sentence segments based on preset punctuation marks; performing text type judgment on each text sentence segment, and dividing the text sentence segment into a paraphrase text and a role dialogue text; performing sentiment analysis on the parastyle text and the role dialogue text to generate a corresponding parastyle sentiment identifier and a corresponding role sentiment identifier; calling a first speech synthesis strategy to perform speech synthesis on the paraphrase text according to the paraphrase emotion identifier to obtain a paraphrase speech segment, and calling a second speech synthesis strategy to perform speech synthesis on the role dialogue text according to the role emotion identifier to obtain a role speech segment; and according to the original sequence of the text sentence segments in the book text, splicing the white speech segments and the role speech segments to obtain a target leading audio. By implementing the technical scheme provided by the invention, the artificial intelligence reading experience feeling of the user is improved.

The text embedding model training method and the related device provided in the application, the data processing device obtains the paraphrase vector of the target vocabulary and the initial vector of the sample text, inputs the paraphrase vector of the target vocabulary and the initial vector of the sample text into the first neural network model to be trained for training, and obtains a text embedding model having an embedding vector conversion function. Since the sample text includes the target vocabulary, and the paraphrase vector of the target vocabulary is an embedding vector representing the paraphrase information of the target vocabulary, the paraphrase information is used as prior knowledge, so that the trained text embedding model has a better embedding vector conversion effect.

The invention relates to the technical field of natural language processing, and discloses a context semantic recognition method and system based on user behaviors, and the method comprises the steps: collecting the multi-source behavior data of a target user, and extracting the semantic features of the user behaviors; constructing a semantic association graph of the user behaviors, detecting semantic understanding defects of the user behaviors, and setting a defect correction mechanism of the semantic understanding defects; semantic modes of user behaviors in different scenes are identified, and real intentions of a target user in different scenes are analyzed; constructing a causal relationship graph of the user behaviors, analyzing driving factors of the user behaviors, and generating behavior intention paraphrases of the target user; and in combination with the semantic association map, the defect correction mechanism and the behavior intention paraphrasing, executing context semantic recognition processing of the user behavior to obtain a semantic recognition result. According to the method, the implicit intention of the user behavior can be understood, and the accuracy of context semantic recognition is improved.

The application provides a knowledge-rich question generation method and device for robustness of a question and answer system, obtains distilled fact descriptions, paraphrases and synonyms of a to-be-queried entity as injected knowledge, and generates knowledge-rich questions by rewriting existing questions by using an editing mechanism, so that different types of knowledge can be used to expand original questions without changing the meanings of the original questions, and more diversified and more meaningful knowledge-rich questions can be generated. Furthermore, the application also inspiringly provides "diagnosis" information for a question and answer model, and provides a dynamic weight for each injected knowledge, so that the question and answer model pays more attention to a question part containing clue information to predict a correct answer, and pays less attention to a question part containing irrelevant information, so that the performance of the question and answer model on knowledge-rich questions and original questions can be effectively improved by dynamically adjusting the weight.

The application provides a training method and device of an open information extraction model, comprising: obtaining a target data set with natural language sentences as samples; generating paraphrases of each natural language sentence in the target data set; performing structured knowledge recovery on the paraphrases of each natural language sentence in the target data set to obtain structured knowledge corresponding to each natural language sentence in the target data set; constructing a first data set with the paraphrases and the structured knowledge corresponding to all natural language sentences in the target data set; and training the open information extraction model by using the first data set and the target data set in a noise reduction training mode. The application constructs a syntax-robust training framework based on paraphrase generation and structured knowledge recovery, so that the open information extraction model can be trained on a data set with sufficient and accurate syntax distribution to adapt to real-world scenarios.

This invention proposes a paraphrase generation method based on a controllable latent space diffusion model, comprising the following steps: Step 1, constructing a paraphrase model based on a latent space diffusion model, and training the paraphrase model based on an existing paraphrase text dataset; the existing paraphrase text dataset contains the original sentence and its paraphrase; Step 2, dividing the paraphrase source sentence into key information and non-key information, and constructing enhanced semantic fragment information; Step 3, constructing a controller; Step 4, training the controller; Step 5, combining the paraphrase model based on a latent space diffusion model trained in Step 1 and the controller trained in Step 4 for collaborative reasoning to generate paraphrased sentences, thus completing the paraphrase generation task based on a controllable latent space diffusion model.

A method of interpreting a verbal input, may include: assigning a meaning classification to the verbal input, and a confidence score to the meaning classification; and based on the confidence score corresponding to the meaning classification of the verbal input being less than or equal to a threshold, generating at least one paraphrase of the verbal input using at least one large language model (LLM); assigning the meaning classification to the at least one paraphrase, and the confidence score to the meaning classification; and concatenating the verbal input, the at least one paraphrase, the meaning classification, and the confidence score to generate a concatenated input; inputting the concatenated input into the at least one LLM.

The invention provides a real-time paraphrasing method and device for BIOS options of a server and a storage medium. The method comprises the steps that description documents of the BIOS options of the server are obtained and analyzed to obtain related configuration information of each option of the BIOS; creating nodes and relationships to construct a knowledge graph about BIOS options, hardware information and associated information; a corpus of BIOS options is defined, a Word2Vec model is trained through the corpus of the BIOS options, a word vector model special for the BIOS options is obtained, the word vector model special for the BIOS options is called to search for three words most similar to the BIOS options, and a BIOS option query result is printed; bIOS option data are monitored in real time through a serial port and transmitted to the knowledge graph, and an associated information query result corresponding to the BIOS option data is obtained through a query function; determining paraphrases of BIOS options according to the BIOS option query result and the associated information query result corresponding to the BIOS option data; and uploading the paraphrases of the BIOS options to a user display interface. According to the method, the BIOS options of the server can be accurately paraphrased in real time.

The invention relates to the technical field of information retrieval, in particular to an online query reading method and system for a Chinese language and literature database, which comprises the following steps: extracting word meaning items, quoting literatures, labeling paraphrase lengths and boundaries, screening candidate sets, analyzing the collocation of subject pronouns and verbs, positioning paragraphs and labeling the positions of the word meaning items, extracting phrase structures and comparing version offsets. According to the method, the positioning, the display and the structure alignment of the literature are optimized through semantic mapping, the accuracy and the consistency of the target literature are improved, and related contents are accurately screened in combination with comparison of context information and versions, so that the accuracy and the consistency of the target literature are improved. Compared with the prior art, the method has the advantages that the method is simple and easy to operate, structural deviation between versions is automatically adjusted, dependence on manual keyword setting is reduced, and meanwhile, the generated word meaning comparison prompt chunk optimizes document retrieval experience, so that a user can more quickly and accurately understand vocabulary meanings and contextual relationships, and information acquisition precision and efficiency are improved.

A computing system includes a memory; and processing circuitry in communication with the memory. The processing circuitry is configured to: receive a paraphrase comprising a paraphrase text sample corresponding to an original text sample; and calculate a paraphrase metric value corresponding to the paraphrase, wherein the paraphrase metric value is calculated based on an adequacy score, a novelty score, and a fluency score of the paraphrase, the adequacy score indicating an extent to which the paraphrase text sample preserves a meaning of the original text sample, the novelty score indicating a level of difference between words and characters of the paraphrase text sample and words and characters of the original text sample, and the fluency score indicating an extent to which the paraphrase text sample is devoid of repetition, spelling, and grammatical mistakes.

A dialog understanding method and apparatus, a readable medium, and an electronic device. The method comprises: acquires dialog content and a preset dialog parsing template, the preset dialog parsing template comprising preset description and at least one c or at least one slot, where in the description information is used for describing a paraphrase of each candidate intention when the preset dialog parsing template comprises at least one candidate intention, and describes a paraphrase of each slot when the preset dialog parsing template at least one comprises slot; and uses the dialog content and the dialog parsing template as inputs of a pre-trained target dialog understanding model to obtain a dialog state corresponding to the dialog content.

The invention provides a rewriting model training method and device, a text rewriting method and device and electronic equipment, relates to the technical field of computers, in particular to the technical fields of artificial intelligence, large models, natural language processing and the like, and can be applied to application scenes such as sentiment analysis, recommendation systems, generative search, man-machine conversation systems, intelligent assistants and virtual humans. According to the specific implementation scheme, a first text is obtained; using the initial rewriting model to obtain a first rewriting result for the first text; using the target evaluation model to obtain a first model evaluation result for the first rewriting result; and training the initial rewriting model based on the first model evaluation result to obtain a target rewriting model. By adopting the method and the device, the text rewriting capability of the target rewriting model can be improved, so that when the target rewriting model is utilized to obtain the rewriting result for the target text, the accuracy of the rewriting result can be improved.

The present disclosure relates to a model generation method, a word sense disambiguation method, an apparatus, a medium, and a device. The model generation method includes: obtaining multiple groups of parallel corpora, where each group of parallel corpora includes a first text and a second text that are translations of each other, the first text belongs to a first language, and the second text belongs to a second language; determining multiple first samples according to the multiple groups of parallel corpora and a preset paraphrase set, each first sample including the first text, a first original word in the first text, and a first paraphrase in the preset paraphrase set that matches a translation word of the first original word, the translation word being a word in the second text that matches the first original word, and the first paraphrase belonging to the second language; and generating a first classification model according to the multiple first samples. The present disclosure can reduce the data dependence for generating the first classification model.

The invention relates to the technical field of data processing, in particular to a translation method of a software development kit, which comprises the following steps of: 1, determining a translation target and a translation range; 2, collecting a terminology library; 3, scanning data, obtaining translation paraphrases and establishing a translation space; step 4, generating a software function component and assembling and verifying complete software; 5, checking and modifying a translation result; and 6, generating a translation report. According to the method, a target software development kit is obtained, a software document, an example code, a configuration tool and a software function requirement document are extracted, a translation database is established, terms, professional nouns and emerging concepts in some specific fields are collected, and then the components are input into a function verification space; and assembling and function verification are performed according to the demand document, so that terms, professional nouns or emerging concepts in specific fields can be accurately translated, and the accuracy and reliability of important information translation results are ensured.

The application discloses a kind of low-resource language machine translation method and device based on multilingual semantic understanding driving, method includes the following steps: constructing data set and pre-processing data set;Build semantic representation model, use the paraphrase sentence in paraphrase corpus to train semantic representation model, the vectorization representation of target language monolingual corpus is carried out, and semantic retrieval library is established;Mask language modeling target and inter-sentence similarity constraint are introduced, and enhanced semantic retrieval model is obtained;With enhanced semantic retrieval model, retrieve the candidate sentence similar to target language sentence semantics in semantic retrieval library, and carry out sorting screening, retain Top-K candidate sentence;After screening, candidate sentence is respectively paired with source language sentence, and enhanced parallel corpus is constructed and merged with original parallel corpus, to form extended training set;Neural machine translation model is trained using extended training set, and output translation text.The method of the application can improve semantic consistency, translation quality and cross-language generalization ability.

The invention discloses an LLM term understanding method, system and device and a storage medium, and the method comprises the steps: obtaining and recognizing professional terms in user input, carrying out the LLM input enhancement through constructing scene description for each professional term, sending the enhanced user input into a pre-trained LLM to complete semantic analysis, and carrying out the semantic analysis. And performing multi-dimensional reordering on a semantic analysis result in combination with LLM initial confidence, context alignment and user preference, taking the paraphrase with the highest score as the optimal semantics input this time, performing ambiguity processing on the optimal semantics, completing a semantic analysis process, and realizing iterative optimization based on user feedback. According to the method, the accuracy of preliminary understanding is ensured, the interpretability and the preintervention of the whole term understanding process are improved, the method can adapt to habits and requirements of different users, and continuous iterative optimization can be performed to keep pace with the times.

A method of interpreting a spoken input may include assigning a meaning classification to the spoken input and assigning a confidence score to the meaning classification; and generating at least one paraphrase of the verbal input using at least one large language model (LLM) based on the confidence score corresponding to the meaning classification of the verbal input being less than or equal to a threshold; assigning the meaning classification to the at least one paraphrase and assigning the confidence score to the meaning classification; and concatenating the verbal input, the at least one paraphrase, the meaning classification, and the confidence score to generate a concatenated input; and inputting the cascaded input into the at least one LLM.

The present application relates to entity relation annotation and correction method based on semantic understanding, and the technology comprises: obtaining a to-be-processed text, performing sentence division and semantic unit segmentation on the to-be-processed text, identifying entity mention, candidate relation predicate and semantic trigger; generating a relation proposition unit for a candidate entity pair, the relation proposition unit comprising a subject entity, an object entity and a candidate relation type; performing relation establishment determination on the relation proposition unit based on the semantic trigger, and determining a relation state label; when the relation state label represents relation establishment, outputting corresponding structured relation data as an entity relation annotation result; the present application generates a relation proposition unit for a candidate entity pair based on semantic understanding, first judges whether the relation actually exists in the current text context, thereby reducing the probability of false annotation in negative, speculation, paraphrase, conditional sentence and other scenarios, and improving the accuracy and semantic consistency of the entity relation annotation result.