WO2022141875A1 - Procédé et appareil de reconnaissance d'intention d'utilisateur, dispositif et support de stockage lisible par ordinateur - Google Patents

Procédé et appareil de reconnaissance d'intention d'utilisateur, dispositif et support de stockage lisible par ordinateur Download PDF

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WO2022141875A1
WO2022141875A1 PCT/CN2021/084250 CN2021084250W WO2022141875A1 WO 2022141875 A1 WO2022141875 A1 WO 2022141875A1 CN 2021084250 W CN2021084250 W CN 2021084250W WO 2022141875 A1 WO2022141875 A1 WO 2022141875A1
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intent
preset
success rate
node
intention
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PCT/CN2021/084250
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English (en)
Chinese (zh)
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李志韬
王健宗
程宁
吴天博
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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
    • 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
    • G06F16/3331—Query processing
    • G06F16/334—Query execution
    • G06F16/3346—Query execution using probabilistic model
    • 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
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology

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  • the present application belongs to the technical field of intelligent decision-making, and in particular, relates to a method, apparatus, device and computer-readable storage medium for identifying user intent.
  • the dialogue system is a human-computer interaction system based on natural language.
  • Intent recognition is an important part of the human-computer interaction system. It converts the content of the user's dialogue into a way that the computer can understand.
  • the recognized intent will directly affect the robot's next sentence. Whether what is said is relevant to what the user expresses, and whether the customer is satisfied.
  • intent recognition mainly includes two parts: intent detection and extraction of semantic slots.
  • the traditional methods of intent recognition have ranged from Hidden Markov Model (HMM), conditional random fields (CRF), and Support Vector Machine (SVM) to more popular in the past decade. Both the convolutional neural network and the recurrent neural network have good experimental results.
  • HMM Hidden Markov Model
  • CRF conditional random fields
  • SVM Support Vector Machine
  • the traditional intent recognition is to use the classifier to select the intent with the highest probability as the final intent.
  • some intent recognition errors may be caused by occasional speech recognition errors. Data, and accurately determine the user's target intention is an urgent problem to be solved at present.
  • One of the purposes of the embodiments of the present application is to provide a method, device, device and computer-readable storage medium for identifying user intent, aiming to solve the technical problem that the intent of the voice data input by the user can be accurately determined according to the voice data of the user .
  • a first aspect of the embodiments of the present application provides a method for identifying user intent, the method comprising:
  • each of the preset intent tags determines a preset number of candidate intent tags from a plurality of the preset intent tags
  • the candidate intent label with the highest dialogue success rate is determined as the intent label of the voice data input by the user.
  • a second aspect of the embodiments of the present application provides a user intent identification device, the user intent identification device includes an acquisition module, a generation module, a screening module, a first determination module, a second determination module, and a third determination module, wherein:
  • the obtaining module is used to obtain text information corresponding to the voice data input by the user;
  • the generating module is configured to input the text information into a preset intent classification model to obtain output probabilities of a plurality of preset intent labels used to represent voice intents;
  • the screening module configured to determine a preset number of candidate intent tags from a plurality of the preset intent tags according to the output probability of each of the preset intent tags;
  • the first determining module is configured to determine the dialog success rate of each intent node in the preset intent knowledge graph, wherein the preset intent knowledge graph is generated according to historical dialog data;
  • the second determining module configured to determine the dialogue success rate of each of the candidate intent tags according to the dialogue success rate of each of the intent nodes
  • the third determining module is configured to determine the candidate intent label with the highest dialogue success rate as the intent label of the voice data input by the user.
  • a third aspect of the embodiments of the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program When realized:
  • each of the preset intent tags determines a preset number of candidate intent tags from a plurality of the preset intent tags
  • the candidate intent label with the highest dialogue success rate is determined as the intent label of the voice data input by the user.
  • a fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement:
  • each of the preset intent tags determines a preset number of candidate intent tags from a plurality of the preset intent tags
  • the candidate intent label with the highest dialogue success rate is determined as the intent label of the voice data input by the user.
  • a fifth aspect of the embodiments of the present application further provides a computer program product, when the computer program product is run on a computer device, the computer device can implement in real time:
  • each of the preset intent tags determines a preset number of candidate intent tags from a plurality of the preset intent tags
  • the candidate intent label with the highest dialogue success rate is determined as the intent label of the voice data input by the user.
  • the embodiments of the present application include the following advantages:
  • the output probabilities of multiple preset intent labels used to represent the voice intent are obtained;
  • the output probability of the preset intent label determine a preset number of candidate intent labels from multiple preset intent labels; then determine the dialog success rate of each intent node in the preset intent knowledge graph;
  • the dialog success rate determines the dialog success rate of each candidate intent label; and the candidate intent label with the highest dialog success rate is determined as the intent label of the speech data input by the user.
  • the method can obtain the output probability of multiple preset intent labels through the preset intent classification model. Combined with the output probability of multiple preset intent labels and the dialogue success rate of each intent node in the preset intent knowledge graph, it can accurately Identify the user's target intent label.
  • FIG. 1 is a schematic flowchart of steps of a method for identifying user intent provided by an embodiment of the present application
  • FIG. 2 is a schematic block diagram of a preset intent classification model provided by an embodiment of the present application.
  • FIG. 3 is a schematic flow chart of sub-steps of the method for identifying user intent in FIG. 1;
  • FIG. 4 is a schematic diagram of a scenario of a knowledge graph of a preset intent provided by an embodiment of the present application
  • FIG. 5 is a schematic block diagram of an apparatus for identifying user intent according to an embodiment of the present application.
  • FIG. 6 is a schematic block diagram of sub-modules of the device for identifying user intent in FIG. 5;
  • FIG. 7 is a schematic structural block diagram of a computer device according to an embodiment of the present application.
  • Embodiments of the present application provide a method, apparatus, device, and computer-readable storage medium for identifying user intent.
  • the method for identifying the user intent can be applied to a terminal device, and the terminal device can be an electronic device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device.
  • FIG. 1 is a schematic flowchart of steps of a method for identifying user intent provided by an embodiment of the present application.
  • the method for identifying user intent includes steps S101 to S105.
  • Step S101 Acquire text information corresponding to the voice data input by the user, and input the text information into a preset intent classification model to obtain output probabilities of multiple preset intent labels representing the voice intent.
  • the preset intent classification model is a pre-trained model, and the preset intent classification model includes a plurality of neural network layers, and the neural network layers at least include at least one of the following: a vector extraction layer, a delay neural network layer, and a ReLU layer , residual network layer, summation layer, recurrent neural network layer, dropout layer and Solfmaxlayer layer.
  • the text information corresponding to the voice data input by the user is obtained, the text information is input into the vector extraction layer, multiple word vectors are obtained, and the multiple word vectors are input into the delay neural network layer , extract multiple word vector features, and input multiple word vector features to the ReLU layer to process multiple word vector features, reduce the gradient disappearance of word vector features, and obtain semantic label vectors.
  • Input word vectors to the summation layer to obtain a plurality of preliminary intent label vectors input a plurality of the preliminary intent label vectors to the recurrent neural network layer, obtain a plurality of candidate intent label vectors, and input the plurality of candidate intent label vectors to dropout layer to obtain multiple preset intent label vectors, and input multiple intent label vectors to the Solfmaxlayer layer to obtain the output probability of the preset intent labels.
  • the vector extraction layer can be selected according to the actual situation.
  • the vector extraction layer is a Word2Vec model
  • the delay neural network layer and the ReLU layer also include a residual network layer.
  • the residual network layer makes the delay neural network
  • the parameter processing of the layer and the ReLU layer is more accurate.
  • the dropout layer can prevent the candidate intent label vector from overfitting and improve the accuracy of the output intent label vector.
  • the training method of the preset intent classification model may be: acquiring sample text information, labeling the sample text information according to the category identifier corresponding to the output probability of the preset intent label, so as to construct sample data, and based on the sample data, neural
  • the network model is iteratively trained until the neural network mode converges, thereby obtaining the preset intent classification model.
  • the above-mentioned neural network models include convolutional neural network models, cyclic neural network models, and cyclic convolutional neural network models. Of course, other network models can also be used for training to obtain a preset intent classification model, which is not specifically limited in this application.
  • the text information corresponding to the voice data input by the user is acquired, and the text information is input into the preset intent classification model to obtain the output probabilities of multiple preset intent labels.
  • the output probability of multiple preset intent labels can be accurately and quickly determined, which greatly improves the user experience.
  • the method of acquiring the text information corresponding to the voice data input by the user is: acquiring the voice input by the user, and inputting the voice into a preset voice recognition model to obtain the text information.
  • the preset speech recognition model is a pre-trained neural network model, which is not specifically limited in this application.
  • the text information corresponding to the voice data transmitted by other devices is obtained, and the text information corresponding to the voice data input by the user is obtained. It can be understood that there are other ways of acquiring the text information corresponding to the voice data of the user data, which is not specifically limited in this application.
  • Step S102 according to the output probability of each of the preset intent tags, determine a preset number of candidate intent tags from a plurality of the preset intent tags.
  • the candidate intent label is the intent label whose intent is closer to the user's intent.
  • the plurality of preset intent tags are sorted in descending order of output probability to obtain an intent tag queue; the preset intent tags are sequentially selected from the intent tag queue until a preset intent tag is obtained. the number of said candidate intent labels.
  • the preset intent label may be set according to the actual situation, which is not specifically limited in this application. For example, the preset intent label may be set to 5.
  • the output probability of the preset intent tag 1 is 10%
  • the output probability of the preset intent tag 2 is 20%
  • the output probability of the preset intent tag 3 is 5%
  • the output probability of the preset intent tag 4 is 12%.
  • the output probability of preset intent tag 5 is 7%
  • the output probability of preset intent tag 6 is 18%
  • the output probability of preset intent tag 7 is 25%
  • the output probability of preset intent tag 8 is 14%
  • the output probability of the preset intent label 9 is 4%
  • the output probability of the preset intent label 10 is 28%. According to the probability of each preset intent label, the 10 intent labels are sorted in descending order to obtain the intent labels.
  • the queue is [preset intent tag 10, preset intent tag 7, preset intent tag 2, preset intent tag 6, preset intent tag 8, preset intent tag 4, preset intent tag 1, preset intent tag 5 , preset intent tag 3, preset intent tag 9], the number of acquired preset intent tags is 5, select the first 5 candidate intent tags from the intent tag queue, and obtain the candidate intent tags as preset intent tags 10, preset intent tags Preset intent tag 7, preset intent tag 2, preset intent tag 6, and preset intent tag 8. By sorting the preset intent labels, candidate intent labels can be quickly selected.
  • Step S103 determining the dialog success rate of each intent node in the preset intent knowledge graph, wherein the preset intent knowledge graph is generated according to historical dialog data.
  • the preset intent knowledge graph is generated according to historical dialogue data. Specifically, all historical dialog data is collected, the historical dialog data is classified, and the associated dialog data is associated to obtain the preset intent knowledge graph.
  • a preset intent knowledge graph is obtained, wherein the preset intent knowledge graph is generated according to historical dialogue data; the success of the intent node corresponding to each intent node in the preset intent knowledge graph is rate as the dialog success rate for each intent node.
  • the dialog success rate of each intent node can be accurately determined by the preset intent knowledge graph.
  • step S103 includes sub-steps S1031 to S1034 .
  • Sub-step S1031 Acquire multiple flow paths of each intention node from the preset intention knowledge graph, and count the number of the multiple flow paths, wherein each flow path includes multiple intention nodes.
  • each flow path includes multiple intent nodes.
  • the preset intent knowledge graph includes intent node a, intent node b, intent node c, intent node d, intent node e, intent node f, and intent node g, and the flow of intent node a.
  • the path includes the flow path where intent node a connects intent node b and intent node c, intent node a connects intent node b connects intent node e connects intent node f, and intent node a connects intent node b connects intent node e connects intent node.
  • intent node a connects intent node b connects intent node e connects intent node g's flow path
  • intent node a connects intent node b connects intent node e connects intent node f
  • the flow path and intent node a connects intent node d
  • the flow path connecting intention node g, the flow path of intention node b includes the flow path connecting intention node b to intention node c, the flow path connecting intention node b to intention node e and the flow path of intention node b connecting intention node e to the intention node
  • the flow path of g is connected to the flow path of intention node b and the flow path of intention node e is connected to the flow path of intention node a.
  • the flow path of intention node e includes the flow path of intention node e connecting intention node a, and the flow path and intention of intention node e connecting intention node f.
  • Node e is connected to the flow path of intention node g
  • the flow path of intention node d includes the flow path of intention node connected to intention node g
  • intention node c and intention node f have no flow path.
  • the number of paths of the flow path of the intention node a is 5, the number of paths of the flow path of the intention node b is 4, and the number of paths of the flow path of the intention node c is 0
  • the number of paths of the flow path of the intent node d is 1, the number of paths of the flow path of the intention node e is 3, the number of paths of the flow path of the intention node f is 0, and the number of paths of the flow path of the intention node g is 0
  • the number of paths is 0.
  • Sub-step S1032 Determine the flow path whose attribute identifier of the last intent node is the preset attribute identifier as a successful flow path.
  • the attribute identifier of the intent node is a keyword set according to the actual situation, for example, keywords such as time, place, and event.
  • the flow path identified by the preset attribute is determined as the flow path that has an attribute of the last intent node in the flow path.
  • the preset attribute identifier is time, and if the last intent node in the flow path is a flow path of time, the flow path is a successful flow path.
  • Sub-step S1033 Count the number of successful flow paths among the multiple flow paths of each intention node.
  • the number of paths of the multiple flow paths of each intent node is determined according to the preset intent knowledge graph.
  • the attribute identifier of the last intent node in the multiple flow paths of each intent node is queried from the preset intent knowledge graph as g.
  • the number of where the attribute of the last intent node in the multiple flow paths of the intent node a is 2, and the attribute of the last intent node in the multiple flow paths of the intent node b is the number of g is 1 , the attribute identifier of the last intent node in the multiple flow paths of the intent node c is 0, the number of the attribute identifier of the last intent node in the multiple flow paths of the intent node d is 1, and the intent node e
  • the attribute identifier of the last intent node in the multiple flow paths is 1, the attribute identifier of the last intent node in the multiple flow paths of the intent node f is 0, and the number of intent node g is multiple flow paths
  • the number of successful circulations of intent node a is 2
  • the number of successful circulations of intent node b is 1
  • the number of successful circulations of intent node c is obtained.
  • the number of successful circulations obtained by intent node d is 1
  • the number of successful circulations obtained by intent node e is 1
  • the number of successful circulations obtained by intent node f is 0, and the number of successful circulations obtained by intent node f is 1 Second-rate.
  • Sub-step S1034 Calculate the percentage of the number of successful circulation paths in the multiple circulation paths of each intention node to the number of all circulation paths, and use the calculated percentage as the dialogue success rate of each intention node.
  • the number of successful circulation of intent node a is 2, the number of successful circulation of intent node b is 1, the number of successful circulation of intent node c is 0, the number of successful circulation of intent node d is 1, and the number of successful circulation of intent node e1 2 times, the number of successful circulation of intention node f is 0, the number of successful circulation of intention node g is 0, the number of paths of the circulation path of intention node a is 5, and the number of paths of the circulation path of intention node b is 4
  • the number of paths for the flow path of the intent node c is 0, the number of paths for the flow path of the intention node d is 1, the number of paths of the flow path of the intention node e is 3, and the number of paths of the flow path of the intention node f
  • the number of paths is 0, and the number of paths of the flow path of intention node g is 0.
  • the number of successful flow of intention node a accounts for 40% of the corresponding number of paths
  • the number of successful flow of intention node b accounts for 40% of the corresponding number of paths.
  • 25% of the number of paths the number of successful circulation of intention node c accounts for 0% of the number of corresponding paths
  • the number of successful circulation of intention node d accounts for 100% of the number of corresponding paths
  • the number of successful circulation of intention node e accounts for the corresponding number of paths.
  • the number of successful circulation of intention node f accounts for 0% of the number of corresponding paths
  • the number of successful circulation of intention node g accounts for 100% of the number of corresponding paths
  • the number of intention nodes a, b, c , d, e, f, and g’s successful flow times account for the number of corresponding paths.
  • the dialog success rate for determining intent node d is 100%
  • the dialog success rate for determining intent node e is 33.3%
  • the dialog success rate for determining intent node f is 0%
  • the dialog success rate for determining intent node g is 100%.
  • Step S104 Determine the dialog success rate of each candidate intent tag according to the dialog success rate of each intent node.
  • the success rate of the candidate intent label is between 0 and 100%, and the larger the candidate intent label, the higher the probability of the successful dialogue of the candidate intent label.
  • the dialog success rate of each intent node and the preset intent label corresponding to each intent node are mapped to obtain the dialog success rate of each preset intent label;
  • the tags are mapped to each other, and the dialogue success rate of the mapped preset intent tags is used as the dialogue success rate of the candidate intent tags.
  • Step S105 Determine the candidate intent tag with the highest dialogue success rate as the intent tag of the voice data input by the user.
  • the target intent label is the intent label closest to the user's intent.
  • a plurality of candidate intent tags are sorted according to the dialogue success rate of each candidate intent tag to obtain a candidate intent tag queue, and the candidate intent tag with the highest dialogue success rate is selected from the candidate intent tag queue as the user's target.
  • Intent label The candidate intent labels are sorted by the success rate and the candidate intent label with the highest success rate is selected as the user's target intent label, which greatly improves the accuracy of determining the user's intent.
  • the dialog success rate of candidate intent label 1 is 50%
  • the dialog success rate of candidate intent label 2 is 25%
  • the dialog success rate of candidate intent label 3 is 15%
  • the dialog success rate of candidate intent label 4 is 60%.
  • the dialogue success rate of candidate intent label 5 is 40%
  • the candidate intent label 1, candidate intent label 2, candidate intent label 3, candidate intent label 4 and candidate intent label 5 are sorted according to the conversation success rate of candidate intent labels, and we get
  • the candidate intent label queue is, [candidate intent label 4, candidate intent label 1, candidate intent label 5, candidate intent label 2, candidate intent label 3], from the candidate intent label queue, select the candidate intent label 4 with the highest dialogue success rate as the The user's goal intent label.
  • the user intent recognition method obtains the output probability of multiple preset intent labels used to represent the voice intent by acquiring the text information corresponding to the voice data input by the user, and inputting the text information into the preset intent classification model. ; Then according to the output probability of each preset intent tag, determine a preset number of candidate intent tags from multiple preset intent tags; then determine the dialogue success rate of each intent node in the preset intent knowledge graph; The dialog success rate of each intent node determines the dialog success rate of each candidate intent label; and the candidate intent label with the highest dialog success rate is determined as the intent label of the speech data input by the user.
  • the method can obtain the output probability of multiple preset intent labels through the preset intent classification model. Combined with the output probability of multiple preset intent labels and the dialogue success rate of each intent node in the preset intent knowledge graph, it can accurately Identify the user's target intent label.
  • FIG. 5 is a schematic block diagram of an apparatus for recognizing user intent according to an embodiment of the present application.
  • the user intent recognition apparatus 200 includes an acquisition module 210 , a generation module 220 , a screening module 230 , a first determination module 240 , a second determination module 250 and a third determination module 260 , wherein,
  • the obtaining module 210 is configured to obtain text information corresponding to the voice data input by the user.
  • the generating module 220 is configured to input the text information into a preset intent classification model to obtain output probabilities of multiple preset intent labels used to represent the voice intent.
  • the screening module 230 is configured to determine a preset number of candidate intent tags from a plurality of the preset intent tags according to the output probability of each preset intent tag.
  • the first determining module 240 is configured to determine the dialog success rate of each intent node in the preset intent knowledge graph, wherein the preset intent knowledge graph is generated according to historical dialog data.
  • the second determination module 250 is configured to determine the dialogue success rate of each of the candidate intent tags according to the dialogue success rate of each of the intent nodes.
  • the third determining module 260 is configured to determine the candidate intent label with the highest dialogue success rate as the intent label of the voice data input by the user.
  • the screening module 230 further includes the following sub-modules:
  • the sorting sub-module is used for sorting a plurality of the preset intent tags according to the descending output probability to obtain an intent tag queue.
  • a selection sub-module configured to sequentially select the preset intent tags from the intent tag queue until a preset number of the candidate intent tags are obtained.
  • the first determining module 240 further includes the following sub-modules:
  • the knowledge graph acquisition sub-module is used to acquire the preset intent knowledge graph.
  • a sub-module is set for taking the success rate of the intent node corresponding to each intent node in the preset intent knowledge graph as the dialog success rate of each intent node.
  • the first determination module 240 includes an acquisition sub-module 241, a statistics module 242, a determination sub-module 243 and a calculation module 244, wherein:
  • the acquisition sub-module 241 is configured to acquire multiple flow paths of each intent node from the preset intent knowledge graph.
  • the statistics module 242 is configured to count the number of the multiple flow paths, wherein each flow path includes multiple intent nodes.
  • the determining submodule 243 is configured to determine the flow path whose attribute identifier of the last intent node is the preset attribute identifier as a successful flow path.
  • the statistics module 242 is further configured to count the number of successful flow paths in the multiple flow paths of each intention node.
  • the calculation module 244 is configured to calculate the percentage of the number of successful circulation paths in the multiple circulation paths of each intention node to the number of all circulation paths, and use the calculated percentage as the dialogue success rate of each intention node.
  • the second determining module 250 further includes the following sub-modules:
  • the first mapping sub-module is configured to map the dialogue success rate of each of the intent nodes and the preset intent labels corresponding to each of the intent nodes to obtain the dialog success rate of each preset intent label.
  • the second mapping submodule is configured to map the candidate intent tag with the preset intent tag, and use the mapped dialog success rate of the preset intent tag as the dialog success rate of the candidate intent tag.
  • the preset intent classification model includes a vector extraction layer, a time-delay neural network layer, a ReLU layer, a residual network layer, a summation layer, a recurrent neural network layer, a dropout layer, and a Solfmaxlayer layer; the generating Module 220 also includes the following sub-modules:
  • the first input sub-module is used for inputting the text information to the vector extraction layer to obtain a plurality of word vectors.
  • the second input sub-module is used for inputting a plurality of the word vectors to the time delay neural network layer, and extracting a plurality of word vector features.
  • the third input sub-module is used for inputting a plurality of the word vector features to the ReLU layer to obtain a semantic label vector.
  • the fourth input sub-module is configured to input the semantic label vector and a plurality of the word vectors into the summation layer to obtain a plurality of preliminary intent label vectors.
  • the fifth input sub-module is used for inputting a plurality of the preliminary intent label vectors to the recurrent neural network layer to obtain a plurality of candidate intent label vectors.
  • the seventh input sub-module is used for inputting a plurality of the candidate intent label vectors to the dropout layer to obtain a plurality of preset intent label vectors.
  • the eighth input sub-module is used for inputting a plurality of the intent label vectors to the Solfmaxlayer layer to obtain the output probability of the preset intent label.
  • the apparatuses provided by the above embodiments may be implemented in the form of a computer program, and the computer program may be executed on the computer device as shown in FIG. 7 .
  • FIG. 7 is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.
  • the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.
  • the nonvolatile storage medium can store operating systems and computer programs.
  • the computer program includes program instructions that, when executed, can cause the processor to execute any method for identifying user intent.
  • This network interface is used for communication.
  • FIG. 7 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.
  • the bus is, for example, an I2C (Inter-integrated Circuit) bus
  • the memory can be a Flash chip, a read-only memory (ROM, Read-Only Memory) magnetic disk, an optical disk, a U disk or a mobile hard disk, etc.
  • the processor can be Central Processing Unit (CPU)
  • the processor can also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), application specific integrated circuits (Application Specific Integrated Circuits, ASICs), field programmable gates Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like.
  • a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements when executing the computer program:
  • each of the preset intent tags determines a preset number of candidate intent tags from a plurality of the preset intent tags
  • the candidate intent label with the highest dialogue success rate is determined as the intent label of the voice data input by the user.
  • the processor when executing the computer program, implements:
  • the preset intent tags are sequentially selected from the intent tag queue until a preset number of the candidate intent tags are obtained.
  • the processor when executing the computer program, implements:
  • the success rate of the intent node corresponding to each intent node in the preset intent knowledge graph is taken as the dialog success rate of each intent node.
  • the processor when executing the computer program, implements:
  • each intention node from the preset intention knowledge graph, and count the number of the multiple flow paths, wherein each flow path includes multiple intention nodes;
  • the processor when executing the computer program, implements:
  • the candidate intent tag is mapped with the preset intent tag, and the dialog success rate of the mapped preset intent tag is used as the dialog success rate of the candidate intent tag.
  • the preset intent classification model includes a vector extraction layer, a delay neural network layer, a ReLU layer, a residual network layer, a summation layer, a recurrent neural network layer, a dropout layer, and a Solfmaxlayer layer; the processing When the computer executes the computer program, it realizes:
  • a plurality of the intent label vectors are input to the Solfmaxlayer layer to obtain the output probability of the preset intent label.
  • Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, the computer program includes program instructions, and the method implemented when the program instructions are executed may refer to this document Various embodiments of methods for identifying user intent are claimed. specific:
  • each of the preset intent tags determines a preset number of candidate intent tags from a plurality of the preset intent tags
  • the candidate intent label with the highest dialogue success rate is determined as the intent label of the voice data input by the user.
  • the computer program when executed by the processor, further implements:
  • the preset intent tags are sequentially selected from the intent tag queue until a preset number of the candidate intent tags are obtained.
  • the computer program when executed by the processor, further implements:
  • the success rate of the intent node corresponding to each intent node in the preset intent knowledge graph is taken as the dialog success rate of each intent node.
  • the computer program when executed by the processor, further implements:
  • each intention node from the preset intention knowledge graph, and count the number of the multiple flow paths, wherein each flow path includes multiple intention nodes;
  • the computer program when executed by the processor, further implements:
  • the candidate intent tag is mapped with the preset intent tag, and the dialog success rate of the mapped preset intent tag is used as the dialog success rate of the candidate intent tag.
  • the preset intent classification model includes a vector extraction layer, a time-delay neural network layer, a ReLU layer, a residual network layer, a summation layer, a recurrent neural network layer, a dropout layer, and a Solfmaxlayer layer; the computer When the program is executed by the processor, it also implements:
  • a plurality of the intent label vectors are input to the Solfmaxlayer layer to obtain the output probability of the preset intent label.
  • the computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as a hard disk or a memory of the computer device.
  • the computer-readable storage medium may be non-volatile or volatile.
  • the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) ) card, Flash Card, etc.

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Abstract

La présente invention, qui relève du domaine technique de la prise de décision intelligente, concerne un procédé et un appareil de reconnaissance d'intention d'utilisateur, un dispositif et un support de stockage lisible par ordinateur. Le procédé comprend : l'obtention d'informations textuelles correspondant à des données de paroles entrées par un utilisateur, et l'introduction des informations textuelles dans un modèle prédéfini de classification d'intention pour obtenir des probabilités de sortie d'une pluralité d'étiquettes d'intention prédéfinies pour représenter des intentions de paroles (S101) ; la détermination d'un nombre prédéfini d'étiquettes d'intention candidates à partir de la pluralité d'étiquettes d'intention prédéfinies selon la probabilité de sortie de chaque étiquette d'intention prédéfinie (S102) ; la détermination d'un taux de succès de dialogue de chaque nœud d'intention dans un graphe prédéfini de connaissance d'intention ; la détermination d'un taux de succès de dialogue de chaque étiquette d'intention candidate selon le taux de succès de dialogue de chaque nœud d'intention (S104) ; et la détermination de l'étiquette d'intention candidate ayant le taux de succès de dialogue le plus élevé en tant qu'étiquette d'intention des données de paroles entrées par l'utilisateur (S105). Une étiquette d'intention cible d'un utilisateur est déterminée par la combinaison du modèle prédéfini de classification d'intention avec le taux de succès de dialogue de chaque nœud d'intention dans le graphe prédéfini de connaissance d'intention.
PCT/CN2021/084250 2020-12-30 2021-03-31 Procédé et appareil de reconnaissance d'intention d'utilisateur, dispositif et support de stockage lisible par ordinateur WO2022141875A1 (fr)

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CN113377969B (zh) * 2021-08-16 2021-11-09 中航信移动科技有限公司 意图识别数据处理系统
CN113593533B (zh) * 2021-09-10 2023-05-02 平安科技（深圳）有限公司 基于意图识别的流程节点跳转方法、装置、设备及介质
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