WO2021135455A1 - Procédé de rappel sémantique, appareil, dispositif informatique et support d'enregistrement - Google Patents

Procédé de rappel sémantique, appareil, dispositif informatique et support d'enregistrement Download PDF

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WO2021135455A1
WO2021135455A1 PCT/CN2020/118454 CN2020118454W WO2021135455A1 WO 2021135455 A1 WO2021135455 A1 WO 2021135455A1 CN 2020118454 W CN2020118454 W CN 2020118454W WO 2021135455 A1 WO2021135455 A1 WO 2021135455A1
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sentence vector
online
candidate
vector
query data
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Chinese (zh)
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骆迅
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • This application relates to the field of artificial intelligence technology, in particular to a semantic recall method, device, computer equipment and storage medium.
  • the semantic recall model is widely used in AI question answering systems.
  • AI question answering systems are used in more and more places to replace manual question answering to improve processing efficiency.
  • the semantic recall model is mainly based on traditional deep learning models, such as CNN, LSTM, and ESTM models.
  • the purpose of the embodiments of the present application is to propose a semantic recall method, device, computer equipment, and storage medium, aiming to solve the technical problem of low efficiency of semantic recall model processing corpus data.
  • an embodiment of the present application provides a semantic recall method, which adopts the following technical solutions:
  • a semantic recall method includes the following steps:
  • the candidate sentence vectors are sorted in descending order according to the similarity, and the answer to the candidate question corresponding to the first-ranked candidate sentence vector is returned as the correct answer.
  • an embodiment of the present application also provides a semantic recall device, which adopts the following technical solutions:
  • the first obtaining module is configured to obtain the online sentence vector corresponding to the online query data based on the sentence vector generator when the online query data is received;
  • the second obtaining module is used to obtain the stored candidate sentence vector
  • a splicing module configured to match the online sentence vector and the candidate sentence vector based on a sentence vector splicer to obtain the similarity between the online sentence vector and the candidate sentence vector;
  • the sorting module is configured to sort the candidate sentence vectors in descending order according to the similarity, and return the answer to the candidate question corresponding to the candidate sentence vector ranked first as the correct answer.
  • an embodiment of the present application also provides a computer device, including a memory and a processor, and computer-readable instructions stored in the memory and capable of running on the processor, and the processor executes
  • the computer-readable instructions implement the steps of the semantic recall method as described below:
  • the candidate sentence vectors are sorted in descending order according to the similarity, and the answer to the candidate question corresponding to the first-ranked candidate sentence vector is returned as the correct answer.
  • embodiments of the present application also provide a computer-readable storage medium, the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the following Steps of semantic recall method:
  • the candidate sentence vectors are sorted in descending order according to the similarity, and the answer to the candidate question corresponding to the first-ranked candidate sentence vector is returned as the correct answer.
  • the online query data is the input sentence
  • the online query data corresponding to the online query data is obtained based on the sentence vector generator.
  • Sentence vector the online sentence vector is the vector form data corresponding to the online query data; obtain the stored candidate sentence vector, where the candidate sentence vector is the sentence vector corresponding to the candidate question stored in the database in advance; based on the sentence vector
  • the splicer matches the online sentence vector and the candidate sentence vector to obtain the similarity between the online sentence vector and the candidate sentence vector; the candidate sentence vector can be screened according to the similarity, so as to filter out the The candidate sentence vector that best matches the online sentence vector.
  • the candidate sentence vectors are sorted in descending order according to the similarity, and the answer to the candidate question corresponding to the candidate sentence vector ranked first is returned as the correct answer.
  • Figure 1 is an exemplary system architecture diagram to which the present application can be applied;
  • Figure 2 is a flowchart of an embodiment of a semantic recall method
  • Figure 3 is a schematic diagram of a sentence vector generator
  • Figure 4 is a schematic diagram of a sentence vector splicer
  • Fig. 5 is a schematic structural diagram of an embodiment of a semantic recall device according to the present application.
  • Fig. 6 is a schematic structural diagram of an embodiment of a computer device according to the present application.
  • semantic recall device 600 first acquisition module 610, second acquisition module 620, splicing module 630, sorting module 640.
  • the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105.
  • the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, and so on.
  • the user can use the terminal devices 101, 102, and 103 to interact with the server 105 through the network 104 to receive or send messages and so on.
  • Various communication client applications such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, and social platform software, may be installed on the terminal devices 101, 102, and 103.
  • the terminal devices 101, 102, and 103 may be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic Video experts compress standard audio layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic image experts compress standard audio layer 4) players, laptop portable computers and desktop computers, etc.
  • MP3 players Moving Picture Experts Group Audio Layer III, dynamic Video experts compress standard audio layer 3
  • MP4 Moving Picture Experts Group Audio Layer IV, dynamic image experts compress standard audio layer 4
  • laptop portable computers and desktop computers etc.
  • the server 105 may be a server that provides various services, for example, a background server that provides support for pages displayed on the terminal devices 101, 102, and 103.
  • the semantic recall method provided in the embodiments of the present application is generally executed by the server/terminal, and correspondingly, the semantic recall device is generally installed in the server/terminal device.
  • terminals, networks, and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks, and servers according to implementation needs.
  • the semantic recall method includes the following steps:
  • Step S200 when receiving online query data, obtain an online sentence vector corresponding to the online query data based on the sentence vector generator;
  • Online query data is real-time query data received online.
  • the online sentence vector corresponding to the online query data is obtained based on the sentence vector generator.
  • the obtained online sentence vector is the sentence vector corresponding to the online query data.
  • the online query data is a sentence
  • the sentence is input to the tokenizer layer in the sentence vector generator
  • the word in the online query data is id based on the tokenizer layer Transformation means converting each word in the sentence into the format of ID.
  • the ID is passed through the embedding layer to obtain the word vector corresponding to each word in the online query data.
  • convolution processing is performed on the word vector to obtain the online sentence vector corresponding to the current online query data.
  • the sentence vector generator is an independent model structure for processing online query data.
  • the traditional deep learning model usually includes a representation layer and an output layer.
  • the representation layer and output layer of the traditional deep learning model are separated, and the part of the representation layer is separated.
  • the corresponding sentence vector generator is obtained.
  • the CNN model Take the CNN model as an example. In the CNN model, the sentence vector generator is shown in Figure 3.
  • q1(char) represents the input layer q1 of the sentence, that is, online query data, and then through the embedding layer to obtain the word vector corresponding to each word in the online query data, the word vector passes (Conv+GlobalMaxPooling)*3, that is, the three-layer convolutional neural network performs convolution processing to obtain the convolution result, where conv is convolution and GlobalMaxPooling is global pooling.
  • Concat splices the obtained convolution results, and outputs the spliced result to obtain the online sentence vector corresponding to the online query data.
  • the results of each layer of convolution must be spliced.
  • the purpose of multi-layer convolution is to make the obtained data more accurate. Therefore, other models may not include concat.
  • Step S300 Obtain a stored candidate sentence vector
  • the candidate sentence vector is pre-stored in the database, and the candidate sentence vector is obtained and stored in advance by the sentence vector generator for the candidate question.
  • candidate questions are obtained in advance, and the sentence vector of the candidate question is generated offline through the offline sentence vector generator.
  • the candidate sentence vector corresponding to the candidate question is obtained, the candidate sentence vector is stored in the database in.
  • Step S400 matching the online sentence vector and the candidate sentence vector based on the sentence vector splicer to obtain the similarity between the online sentence vector and the candidate sentence vector;
  • the similarity between the candidate sentence vector and the online sentence vector is calculated based on the vector splicer. Specifically, the difference feature vectors of the candidate sentence vector and the online sentence vector in different measurement dimensions are calculated, and finally the difference feature vectors in different measurement dimensions are combined and spliced to obtain the final difference between the candidate sentence vector and the online sentence vector Feature vector.
  • regularization processing is performed on the difference feature vector to obtain the similarity between the online sentence vector and the candidate sentence vector.
  • the online sentence vector and candidate sentence vector When the online sentence vector and candidate sentence vector are obtained, the online sentence vector and candidate sentence The vector is input to Diff+Mul+Max; Diff+Mul+Max calculates the difference feature vector of the online sentence vector and candidate sentence vector from the three measurement dimensions of subtraction, multiplication and maximum, thereby obtaining the online sentence
  • the difference feature vector of the vector and the candidate sentence vector in the three dimensions concat splices the difference feature vector calculated in the three measurement dimensions to obtain the final difference feature vector; input the final difference feature vector to 3*( Dense+BatchNormalization+Relu+Dropout) to make it regularize the final difference feature vector obtained by splicing.
  • input the result of the regularization process into Sigmoid
  • Sigmoid is the activation function, and use Indicates that the result of the regularization process is passed through the activation function to obtain the similarity between the online sentence vector and the candidate sentence vector.
  • Step S500 Sort the candidate sentence vectors in descending order according to the similarity, and return the answer to the candidate question corresponding to the candidate sentence vector ranked first as the correct answer.
  • the candidate questions corresponding to the candidate sentence vector are sorted in descending order according to the similarity, that is, sorted from large to small.
  • the answer to the candidate question corresponding to the candidate sentence vector with the highest similarity of the sentence vector on the line is selected as the correct answer.
  • the representation layer and output layer of the traditional model are separated into a sentence vector generator and a splicer without changing the accuracy of the original model.
  • the sentence vector generator processes the data, and then through the sentence vector splicer, the processed data and the candidate sentence vector are spliced, without the need for the overall model structure, which increases the amount of concurrency of model processing and improves the model’s ability to process corpus Data processing efficiency and accuracy of question and answer matching.
  • This application belongs to the field of artificial intelligence technology and has good performance in both machine learning and deep learning.
  • step S200 includes:
  • Multi-layer convolution processing is performed on the word vector to obtain the online sentence vector of the online query data.
  • the online query data is a single sentence, where the word vector is the vector corresponding to each word in the single sentence.
  • each word in the online query data is idized according to the word frequency, TF-IDF (term frequency—inverse document frequency) and other characteristics in the online query data to obtain the The ID corresponding to each word in the online query data.
  • the word vector corresponding to each ID in the online query data is obtained.
  • the embedding layer there is a mapping relationship between the ID and the word vector.
  • the embedding layer is passed The word vector corresponding to each word in the online query data can be obtained.
  • the convolution result is a set of sentence vectors corresponding to the online query data.
  • a set of sentence vectors cannot fully reflect the characteristic information of the current online query data. Therefore, all the word vectors obtained in the online query data are subjected to multi-layer convolution processing to obtain multiple sets of convolution results. The multiple sets of convolution results obtained are spliced together, and the final result obtained is the online sentence vector corresponding to the online query data.
  • the online sentence vector of the online query data is obtained according to the word vector. There is no need for a complete model structure. Only a sentence vector generator is needed to obtain the corresponding online sentence vector, which improves The model's processing efficiency on corpus data has further improved the concurrency of model processing.
  • acquiring the word vector of the online query data includes:
  • the ID is feature-encoded based on the embedding layer of the sentence vector generator to obtain a word vector corresponding to each word in the online query data.
  • the token analysis layer is the tokenizer layer, and each word in the received online query data can be IDized according to the tokenizer layer. Specifically, when online query data is received, the word frequency, tfidf and other characteristics of the online query data are obtained. Based on the characteristics, the tokenizer layer can ID each word in the online query data, for example, The ID of the word division with the word frequency of 5 is 001. After the ID of each word in the online query data is completed in the tokenizer layer, the ID of each word obtained is input to the embedding layer, that is, the embedding layer.
  • the Embedding layer determines the word vector corresponding to each word according to the ID, that is, based on the embedding layer, the ID of each word is feature-coded, and the mapping between each word and the multi-dimensional space is determined, thereby obtaining the currently input online query data The word vector corresponding to each word in.
  • the analysis and extraction of online query data according to the tag analysis layer and the embedding layer are realized, the efficiency and accuracy of online query data analysis are improved, and the corresponding matching data obtained from online query data is further improved. (That is, the correct answer) efficiency and accuracy.
  • performing multi-layer convolution processing on the word vector to obtain the online sentence vector of the online query data includes:
  • the semantic features obtained each time are spliced together to obtain the online sentence vector of the online query data.
  • the word vector corresponding to each word in the online query data is obtained, it is determined that the online query data is based on the semantic feature of the word, and the semantic feature is the logical representation based on the word in the online query data.
  • a convolutional neural network such as a CNN three-layer convolutional neural network
  • the semantic features of the online query data can be extracted based on the obtained word vector.
  • the word vector of each word in the online query data is subjected to convolution processing through the convolutional neural network, and the convolution result obtained is the semantic feature of the online query data based on the word, and the semantic feature is also A set of vectors.
  • Multi-layer convolution is performed on the word vector through the convolutional neural network, and the semantic features obtained each time are spliced together to obtain the online sentence vector corresponding to the online query data.
  • a three-layer convolutional neural network is used to perform three-layer convolution on all word vectors in the online query data, and the result of the three-layer convolution, that is, the semantic feature, is spliced together, and the output is the online The online sentence vector corresponding to the query data.
  • the online sentence vector corresponding to the online query data is obtained by stitching according to semantic features, which improves the accuracy of obtaining the online sentence vector corresponding to the online query data, and further improves the The accuracy of the vector matching to get the correct answer.
  • step S300 includes:
  • Candidate questions are pre-collected questions, and the candidate questions are pre-stored in the question library.
  • the candidate question is obtained, the candidate sentence vectors are calculated one by one for all candidate questions in the question library.
  • the candidate question is calculated offline based on the sentence vector generator, and the calculation process is the same as that of the online sentence vector.
  • the candidate sentence vector can also be calculated offline based on the sentence vector generator without a network connection; for online sentence vectors, the sentence vector generator only performs real-time calculations on the received online questions.
  • the calculation of the candidate sentence vector of the candidate question is realized, and the pre-calculation and storage of the candidate sentence vector saves the matching time during question and answer matching, and improves the efficiency of obtaining answers.
  • the method further includes:
  • the candidate sentence vector is stored in a database in a dictionary in association with the candidate question.
  • the candidate sentence vector When the candidate sentence vector is obtained, the candidate sentence vector is stored in the form of a dictionary. Specifically, each candidate sentence vector corresponds to unique identification information, and the candidate sentence vector and its corresponding candidate question are associated and stored according to the identification information. When extracting the candidate sentence vector and the corresponding candidate question, it can be directly extracted based on the identification information.
  • pre-storage of candidate sentence vectors in the form of a dictionary is realized, which further improves the extraction efficiency of candidate sentence vectors during matching, and saves the duration of question and answer matching.
  • the semantic recall method further includes:
  • the multiplication is to perform dot multiplication on the online sentence vector and the candidate sentence vector, and the result obtained is the difference feature vector of the online sentence vector and the candidate sentence vector in the multiplication measurement dimension;
  • the subtraction is the online sentence The vector and the candidate sentence vector are subtracted, and the result is the difference feature vector of the line sentence vector and the candidate sentence vector in the subtraction measurement dimension;
  • the maximum value is the maximum value of the line sentence vector and the candidate sentence vector ,
  • the maximum value obtained is the feature vector of the difference between the line sentence vector and the candidate sentence vector in the maximum measurement dimension.
  • the difference feature values corresponding to the three measurement dimensions of the multiplication, subtraction, and maximum value are spliced together to obtain the final difference feature vector of the online sentence vector and the candidate sentence vector.
  • the measurement dimension includes, but is not limited to, three measurement dimensions of multiplication, subtraction, and maximum value, and may also include measurement dimensions such as minimum value.
  • the difference feature vector is regularized, and subjected to dense layer dimensionality reduction and activation function sigmoid processing.
  • the sigmoid function can map the variable to between 0 and 1, which can be obtained
  • An output result is a probability value from 0 to 1. According to the probability value, the similarity between the online sentence vector and the candidate sentence vector is measured; if the probability is greater than 0.5, it is determined that the online sentence vector and the candidate sentence vector are similar, otherwise, they are not similar.
  • the splicing and matching of online sentence vectors and candidate sentence vectors is realized, and the processing of the entire model is also not required, which improves the processing efficiency of the model, and the candidate sentence vector with the highest matching degree is determined through the similarity output, and further This greatly improves the accuracy of obtaining answers to questions.
  • the processes in the above-mentioned embodiment methods can be implemented by instructing relevant hardware through computer-readable instructions, which can be stored in a computer-readable storage medium.
  • the computer-readable instructions When executed, they may include the processes of the above-mentioned method embodiments.
  • the aforementioned storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disc, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.
  • this application provides an embodiment of a semantic recall device.
  • the device embodiment corresponds to the method embodiment shown in FIG. Used in various electronic devices.
  • the semantic recall device 600 in this embodiment includes:
  • the first obtaining module 610 is configured to obtain the online sentence vector corresponding to the online query data based on the sentence vector generator when the online query data is received;
  • the first obtaining module 610 includes:
  • the first obtaining unit is configured to obtain the word vector of the online query data based on the sentence vector generator;
  • the first processing unit is configured to perform multi-layer convolution processing on the word vector to obtain the online sentence vector of the online query data.
  • the first acquiring unit further includes:
  • the second processing unit is configured to perform ID processing on each word in the online query data based on the tag analysis layer of the sentence vector generator to obtain an ID corresponding to each word in the online query data;
  • the third processing unit is configured to perform feature encoding on the ID based on the embedding layer of the sentence vector generator to obtain a word vector corresponding to each word in the online query data.
  • the first processing unit further includes:
  • a fourth processing unit configured to perform multi-layer convolution processing on the word vector based on a convolutional neural network to obtain semantic features corresponding to the online query data
  • the first splicing unit is used to splice the semantic features obtained each time together to obtain the online sentence vector of the online query data.
  • Online query data is real-time query data received online.
  • the online sentence vector corresponding to the online query data is obtained based on the sentence vector generator.
  • the obtained online sentence vector is the sentence vector corresponding to the online query data.
  • the online query data is a sentence
  • the sentence is input to the tokenizer layer in the sentence vector generator
  • the word in the online query data is id based on the tokenizer layer Transformation means converting each word in the sentence into the format of ID.
  • the ID is passed through the embedding layer to obtain the word vector corresponding to each word in the online query data.
  • convolution processing is performed on the word vector to obtain the online sentence vector corresponding to the current online query data.
  • the sentence vector generator is an independent model structure for processing online query data.
  • the traditional deep learning model usually includes a representation layer and an output layer.
  • the representation layer and output layer of the traditional deep learning model are separated, and the part of the representation layer is separated.
  • the corresponding sentence vector generator is obtained.
  • the CNN model Take the CNN model as an example. In the CNN model, the sentence vector generator is shown in Figure 3.
  • q1(char) represents the input sentence q1, that is, online query data
  • the word vector passes ( Conv+GlobalMaxPooling)*3, that is, the three-layer convolutional neural network performs convolution processing to obtain the convolution result, where conv is convolution and GlobalMaxPooling is global pooling.
  • Concat splices the obtained convolution results, and outputs the spliced result to obtain the online sentence vector corresponding to the online query data.
  • the results of each layer of convolution must be spliced.
  • the purpose of multi-layer convolution is to make the obtained data more accurate. Therefore, other models may not include concat.
  • the second obtaining module 620 is configured to obtain stored candidate sentence vectors
  • the second obtaining module 620 includes:
  • the second obtaining unit is used to obtain candidate questions stored in the question library
  • the first calculation unit is configured to perform offline calculation on the candidate question based on the sentence vector generator to obtain a candidate sentence vector corresponding to the candidate question.
  • the third acquiring unit is configured to acquire the unique identification information corresponding to each candidate sentence vector
  • the storage unit is configured to store the candidate sentence vector in a dictionary in the form of a dictionary in association with the candidate question in a database according to the identification information.
  • the candidate sentence vector is pre-stored in the database, and the candidate sentence vector is obtained and stored in advance by the sentence vector generator for the candidate question.
  • candidate questions are obtained in advance, and the sentence vector of the candidate question is generated offline through the offline sentence vector generator.
  • the candidate sentence vector corresponding to the candidate question is obtained, the candidate sentence vector is stored in the database in.
  • the splicing module 630 is configured to match the online sentence vector and the candidate sentence vector based on a sentence vector splicer to obtain the similarity between the online sentence vector and the candidate sentence vector;
  • the splicing module includes:
  • the second calculation unit is used to calculate the difference feature vector of the online sentence vector and the candidate sentence vector in the three measurement dimensions of multiplication, subtraction, and maximum;
  • the second splicing unit is used to splice the difference feature vectors in the three measurement dimensions together to obtain the final difference feature vector;
  • a fifth processing unit configured to perform regularization processing on the final difference feature vector to obtain a processing result
  • the sixth processing unit is configured to perform function processing on the processing result to obtain the similarity between the online sentence vector and the candidate sentence vector.
  • the similarity between the candidate sentence vector and the online sentence vector is calculated based on the vector splicer. Specifically, the difference feature vectors of the candidate sentence vector and the online sentence vector in different measurement dimensions are calculated, and finally the difference feature vectors in different measurement dimensions are combined and spliced to obtain the final difference between the candidate sentence vector and the online sentence vector Feature vector.
  • regularization processing is performed on the difference feature vector to obtain the similarity between the online sentence vector and the candidate sentence vector.
  • the online sentence vector and candidate sentence vector When the online sentence vector and candidate sentence vector are obtained, the online sentence vector and candidate sentence The vector is input to Diff+Mul+Max; Diff+Mul+Max calculates the difference feature vector of the online sentence vector and candidate sentence vector from the three measurement dimensions of subtraction, multiplication and maximum, thereby obtaining the online sentence
  • the difference feature vector of the vector and the candidate sentence vector in the three dimensions concat splices the difference feature vector calculated in the three measurement dimensions to obtain the final difference feature vector; input the final difference feature vector to 3*( Dense+BatchNormalization+Relu+Dropout) to make it regularize the final difference feature vector obtained by splicing.
  • input the result of the regularization process into Sigmoid
  • Sigmoid is the activation function, and use Indicates that the result of the regularization process is passed through the activation function to obtain the similarity between the online sentence vector and the candidate sentence vector.
  • the sorting module 640 is configured to sort the candidate sentence vectors in descending order according to the similarity, and return the answer to the candidate question corresponding to the first-ranked candidate sentence vector as the correct answer.
  • the candidate questions corresponding to the candidate sentence vector are sorted in descending order according to the similarity, that is, sorted from large to small.
  • the answer to the candidate question corresponding to the candidate sentence vector with the highest similarity of the sentence vector on the line is selected as the correct answer.
  • FIG. 6 is a block diagram of the basic structure of the computer device in this embodiment.
  • the computer device 6 includes a memory 61, a processor 62, and a network interface 63 that communicate with each other through a system bus. It should be pointed out that only the computer device 6 with components 61-63 is shown in the figure, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions.
  • Its hardware includes, but is not limited to, a microprocessor, a dedicated Integrated Circuit (Application Specific Integrated Circuit, ASIC), Programmable Gate Array (Field-Programmable Gate Array, FPGA), Digital Processor (Digital Signal Processor, DSP), embedded equipment, etc.
  • ASIC Application Specific Integrated Circuit
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Processor
  • the computer device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer device can interact with the user through a keyboard, a mouse, a remote control, a touch panel, or a voice control device.
  • the memory 61 includes at least one type of readable storage medium, the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static memory Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, etc.
  • the computer-readable storage medium may be non-volatile or volatile.
  • the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6.
  • the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart media card (SMC), a secure digital (Secure Digital, SD) card, Flash Card, etc.
  • the memory 61 may also include both the internal storage unit of the computer device 6 and its external storage device.
  • the memory 61 is generally used to store an operating system and various application software installed in the computer device 6, such as computer-readable instructions of a semantic recall method.
  • the memory 61 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips.
  • the processor 62 is generally used to control the overall operation of the computer device 6.
  • the processor 62 is configured to execute computer-readable instructions or process data stored in the memory 61, for example, computer-readable instructions for executing the semantic recall method.
  • the network interface 63 may include a wireless network interface or a wired network interface, and the network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.
  • the computer device realizes that without changing the accuracy of the original model, the representation layer and the output layer of the traditional model are split into a sentence vector generator and a splicer, respectively.
  • a sentence vector generator Only need to process the data through a single sentence vector generator, and then use the sentence vector splicer to splice the processed data and candidate sentence vectors, without the need for the overall model structure, which increases the amount of concurrency of model processing.
  • This application also provides another implementation manner, that is, to provide a computer-readable storage medium that stores a semantic recall process, and the semantic recall process can be executed by at least one processor to enable all The at least one processor executes the steps of the semantic recall method as described above.
  • the computer-readable storage medium realizes that without changing the accuracy of the original model, the representation layer and the output layer of the traditional model are split into a sentence vector generator and a splicer, respectively.
  • sentence vectors only a single sentence vector generator is needed to process the data, and then a sentence vector splicer is used to splice the processed data and candidate sentence vectors, without the need for the overall model structure, which improves the concurrency of model processing.
  • This improves the model’s processing efficiency and the accuracy of Q&A matching when processing corpus data. And can be applied to a variety of different types of models, with mobility and high scalability.
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes several instructions to make a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.
  • a terminal device which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

Abstract

L'invention concerne un procédé de rappel sémantique, appartenant au domaine de l'intelligence artificielle, consistant à : lors de la réception de données d'interrogation en ligne, obtenir, sur la base d'un générateur de vecteur de phrase, un vecteur de phrase en ligne correspondant aux données d'interrogation en ligne (S200) ; obtenir un vecteur de phrase candidat stocké (S300) ; mettre en correspondance le vecteur de phrase en ligne et le vecteur de phrase candidat sur la base d'un assembleur de vecteur de phrase pour obtenir la similarité entre le vecteur de phrase en ligne et le vecteur de phrase candidat (S400) ; trier des vecteurs de phrase candidats par ordre décroissant selon la similarité, et renvoyer, sous la forme d'une réponse correcte, une réponse de premier rang à une question candidate correspondant au vecteur de phrase candidat (S500). Le procédé est tel que sans modification de la précision d'un modèle d'origine, la couche de représentation et la couche de sortie d'un modèle classique sont réparties en un générateur de vecteur de phrase et en un assembleur, ce qui accroît la simultanéité traitée par le modèle, et améliore l'efficacité de traitement du modèle lors du traitement de données de corpus et la précision de mise en correspondance de questions et de réponses.
PCT/CN2020/118454 2020-05-13 2020-09-28 Procédé de rappel sémantique, appareil, dispositif informatique et support d'enregistrement WO2021135455A1 (fr)

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