WO2021164286A1 - Procédé, appareil et dispositif de reconnaissance d'intention d'utilisateur, et support de stockage lisible par ordinateur - Google Patents

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

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Publication number
WO2021164286A1
WO2021164286A1 PCT/CN2020/122059 CN2020122059W WO2021164286A1 WO 2021164286 A1 WO2021164286 A1 WO 2021164286A1 CN 2020122059 W CN2020122059 W CN 2020122059W WO 2021164286 A1 WO2021164286 A1 WO 2021164286A1
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elements
historical
target
training
model
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PCT/CN2020/122059
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English (en)
Chinese (zh)
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朱海军
许开河
王少军
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平安科技（深圳）有限公司
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Publication of WO2021164286A1 publication Critical patent/WO2021164286A1/fr

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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
    • G06F16/35—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

  • This application relates to the field of natural language processing technology, and in particular to a method, device, device, and computer-readable storage medium for recognizing user intentions.
  • the intent recognition of the user query is a very important part.
  • the current intent recognition scheme for the user query such as intent classification and intent retrieval, etc.
  • the current method is basically to identify the important elements of the user query, but the current common element identification methods have great limitations. Either they have strict restrictions on the data to be processed, or they cannot be directly processed. Optimize the target of the element recognition, thereby reducing the accuracy of the element recognition, thereby affecting the final result of the user's query intention recognition.
  • a method for recognizing user intentions includes:
  • Each of the relevant historical elements and the target question are sequentially combined to obtain each target data, and each of the target data is sequentially predicted through the element recognition model to determine whether each of the target data conforms to a preset Require;
  • the relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • a user intention recognition device comprising:
  • the first obtaining module is configured to receive the target question input by the user, and input the target question into the element recognition model to obtain a plurality of historical questions associated with the target question;
  • the second acquisition module is configured to acquire historical elements corresponding to each of the historical questions, and perform duplicate checking processing on each of the historical elements to obtain multiple related historical elements;
  • the prediction module is used to sequentially combine each of the relevant historical elements and the target question to obtain multiple target data, and predict each of the target data in turn through the element recognition model to determine each of the target data Whether the target data meets the preset requirements;
  • the determining module is configured to use the relevant historical elements in the target data that meets the preset requirements as the target question elements corresponding to the target question, and determine the user's intention based on the target question elements.
  • a user intention recognition device comprising: a memory, a processor, and a computer program stored in the memory and running on the processor, wherein:
  • Each of the relevant historical elements and the target question are sequentially combined to obtain multiple target data, and each target data is sequentially predicted through the element recognition model to determine whether each target data meets the forecast Set requirements
  • the relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • a computer-readable storage medium having a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the following steps are implemented:
  • Each of the relevant historical elements and the target question are sequentially combined to obtain multiple target data, and each target data is sequentially predicted through the element recognition model to determine whether each target data meets the forecast Set requirements
  • the relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • FIG. 1 is a schematic diagram of the terminal ⁇ device structure of the hardware operating environment involved in the solution of the embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for identifying user intentions according to this application;
  • FIG. 3 is a schematic diagram of functional modules of the user intention recognition device of this application.
  • FIG. 1 is a schematic diagram of a terminal structure of a hardware operating environment involved in a solution of an embodiment of the present application.
  • the terminal in the embodiment of the present application is a user intention recognition device.
  • the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a magnetic disk memory.
  • the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
  • the terminal may also include a camera, an RF (Radio Frequency, radio frequency) circuit, a sensor, an audio circuit, a WiFi module, and so on.
  • sensors such as light sensors, motion sensors and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor.
  • the ambient light sensor can adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor can turn off the display screen and/or when the terminal device is moved to the ear. Backlight.
  • the terminal device can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which will not be repeated here.
  • terminal structure shown in FIG. 1 does not constitute a limitation on the terminal, and may include more or less components than shown in the figure, or combine some components, or arrange different components.
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a user intention recognition program.
  • the network interface 1004 is mainly used to connect to the back-end server and communicate with the back-end server;
  • the user interface 1003 is mainly used to connect to the client (user side) and communicate with the client;
  • the processor 1001 can be used to call the user intention recognition program stored in the memory 1005 and perform the following operations:
  • Each of the relevant historical elements and the target question are sequentially combined to obtain multiple target data, and each target data is sequentially predicted through the element recognition model to determine whether each target data meets the forecast Set requirements
  • the relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • the present application provides a method for recognizing user intent.
  • the method for recognizing user intent includes the following steps:
  • Step S10 receiving a target question input by a user, and inputting the target question into the element recognition model to obtain a plurality of historical questions associated with the target question;
  • element identification is converted to element search, thereby simplifying the difficulty of the task.
  • Element search can be achieved through multiple queries (questions) and 0/1 classification of elements. 1 means that the element is contained in the query, and 0 means that the element is not contained in the query.
  • the 0/1 classification model requires a large amount of negative sample data, that is, the elements required by the 0/1 classification model can only be obtained through the query, but the negative samples cannot be obtained. data. Therefore, a large number of high-quality negative sample data can be constructed through the es (elasticsearch, open source search engine) framework. Among them, the negative sample data may be data that is associated with the query but is not the same.
  • the self-attention and powerful generalization ability of the bert (language representation model) model can be used to perform 0/1 classification training on the query and elements, which can improve the accuracy of data classification Rate and reduce the demand for training data.
  • the bert model has completed the classification training of the query and elements, it can be clearly detected that the bert model has a huge amount of parameters, and the model training takes a long time, which may easily lead to the bert model not being able to meet the real-time needs of intelligent customer service, so it is necessary
  • the parameters of the model are compressed as much as possible without losing the effect of the model as much as possible.
  • the trained bert model can be compressed through the knowledge distillation technology, and after the compression is completed, the trained and compressed bert model can be released, that is, online personnel can formally apply the released bert model bert model.
  • the knowledge distillation is to induce the training of the student network through the soft goal related to the teacher network of the quotation, and realize the knowledge transfer.
  • the trained bert model is knowledge simplified through knowledge distillation, and only the parameters associated with the query are retained.
  • the target question can be any question entered by the user.
  • the element recognition model can be a model after the bert model has been classified and trained and the knowledge is distilled and compressed and released.
  • the system terminal receives the target question input by the user or the client, it will input the target question into the element recognition model for model training, and obtain each historical question in the knowledge base corresponding to the element recognition model, and retrieve it in each From the historical questions, various historical questions related to the target question are obtained.
  • you can determine each historical question related to the target question by checking whether the text in each historical question is the same as the target question; it can also check whether the meaning of the word in each historical question has and the target The problem is the same to determine the various historical problems related to the target problem, etc.
  • Step S20 Obtain historical elements corresponding to each of the historical questions, and perform duplicate checking processing on each of the historical elements to obtain multiple related historical elements;
  • each historical issue related to the target issue it is also necessary to determine the historical elements corresponding to these historical issues, that is, obtain the historical elements corresponding to each historical issue related to the target issue from the knowledge base, and obtain the historical elements Later, these historical elements can be checked for duplicate processing to detect whether there are two or more elements that are completely the same. If they exist, determine these same elements as repeated elements, and after determining each repeated element, It is necessary to select one element from these repetitive elements and non-repetitive elements together as the relevant historical element corresponding to the target question. Among them, historical elements can be elements marked in historical questions.
  • Step S30 Combine each of the relevant historical elements and the target question in sequence to obtain multiple target data, and use the element recognition model to predict each of the target data in turn to determine each of the target data Whether it meets the preset requirements;
  • each relevant historical element After obtaining each relevant historical element, it is also necessary to sequentially combine each relevant historical element and the target question to obtain the combined target data. For example, when the target question is "Are the ATM transfers free?" and the relevant historical elements obtained are "Teller Machines, Handling Fees, Free", then you can turn “Teller Machines, Handling Fees, Free” and “Teller Machine Transfers” in turn. Is it free?” Combine separately to obtain three target data, so that the three target data can be model-trained through the element recognition model.
  • each target data is predicted through the feature recognition model in turn, and the prediction result is used to determine whether the target data meets the preset requirements, that is, when traversing each target data, the feature recognition model determines whether the currently traversed target data meets the preset requirements Requirements (where the preset requirements can be any requirements set by the user in advance), if the currently traversed target data meets the preset requirements, it can be considered that the target problem in the current traversed target data includes the relevant history in the current traversed target data Elements, and determine the relevant historical elements in the target data currently traversed as the target problem elements corresponding to the target problem.
  • the preset requirements can be any requirements set by the user in advance
  • the target data currently traversed does not meet the preset requirements, it can be considered that the target problem in the target data currently traversed does not include the relevant historical elements in the target data currently traversed, and the current traversed target data can be determined accordingly.
  • the relevant historical element is not the target question element corresponding to the target question. It should be noted that in this embodiment, all target data needs to be predicted through the element recognition model.
  • step S40 relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • the prediction results of each target data predicted by the feature recognition model are obtained, and based on the prediction results, it is determined which target data meets the preset requirements and which target data is not. Preset requirements, and use the relevant historical elements in the target data that meet the preset requirements as the target problem elements corresponding to the target problem, and when each target problem element is obtained, the user's intention can be calculated based on these target problem elements .
  • a plurality of historical questions associated with the target question are obtained; and the historical element corresponding to each of the historical questions is obtained , And perform duplicate checking on each of the historical elements to obtain multiple relevant historical elements; sequentially combine each of the relevant historical elements and the target question to obtain multiple target data, and identify them through the elements
  • the model predicts each of the target data in turn to determine whether each of the target data meets the preset requirements; uses the relevant historical elements in the target data that meets the preset requirements as the target question elements corresponding to the target question, and The user's intention is determined based on the target question element.
  • the target data is obtained, and then each target data is sequentially input into the element recognition model for model training to determine the target problem element, thereby avoiding
  • the phenomenon that the target of element identification cannot be directly optimized occurs, which improves the accuracy of user intent identification, and because element search is performed in the model according to the problem, the obtained value is improved.
  • the accuracy of the elements solves the technical problem of low accuracy when performing element identification in the prior art.
  • the step of sequentially predicting the target data includes:
  • Step a traverse each of the target data in turn, and train the currently traversed target data through the element recognition model to determine whether the target question includes relevant historical elements in the currently traversed target data;
  • Step b if included, determine that the currently traversed target data meets the preset requirements, and use the relevant historical elements in the currently traversed target data as the target problem element corresponding to the target question until each target Data traversal is complete.
  • the relevant historical elements in the target data currently traversed can be used as the target question elements corresponding to the target question, until each target data traversal is completed, that is, it is necessary Determine whether the relevant historical elements in all target data are the target problem elements corresponding to the target problem. However, if the target problem does not include the relevant historical elements in the target data currently traversed, the traversal of the next target data is continued until all target data traversal is completed.
  • the process of obtaining the elements of the target problem is simplified.
  • a third embodiment of the user intention identification method of the present application is proposed.
  • This embodiment is step S10 of the first embodiment of the present application. Before the step of inputting the target question and inputting the target question into the element recognition model, it includes:
  • Step c Obtain a bert model of a language representation model and a plurality of input training questions, and perform classification training on each of the training questions sequentially through the bert model;
  • step d the bert model completed after each classification training is used as a training model, and an element recognition model is determined based on the training model.
  • the bert model that has been trained for each classification can be used as the training model, and the element recognition model can be determined according to this training model. That is, if the training model is directly released, the released training model can be used as a feature recognition model. However, generally due to the huge amount of parameters of the training model, which does not meet the real-time requirements, the training model can be processed first and then released to obtain the features Identify the model.
  • the bert model that has undergone classification training is used as the training model, and the element recognition model is determined according to the training model, thereby ensuring that the obtained element recognition model is effective and usable.
  • the step of sequentially classifying and training each of the training questions through the bert model includes:
  • Step c1 traverse each of the training questions in turn, determine multiple label elements in the training question currently traversed, and obtain multiple negative sample data associated with the training question currently traversed;
  • each training problem is traversed in turn, and each labeling element in the training problem currently traversed is determined, and a large number of high-quality negative sample data associated with the training problem currently traversed can be constructed through es retrieval.
  • the negative sample data is data that is associated with the training problem but is not annotated elements.
  • Step c2 based on each of the negative sample data and each of the annotation elements, and perform classification training on the currently traversed training questions through the bert model, until the traversal of each of the training questions is completed.
  • the bert model can be used to classify and train the current traversed training problem to determine which elements the current traversed training problem contains until the traversal of each training problem is completed, that is Until all training problems have been classified training.
  • the usability of the bert model is determined by obtaining the annotation elements and each negative sample data corresponding to the training problem, and performing classification training through the bert model.
  • the step of determining an element recognition model based on the training model includes:
  • step d1 the training model is compressed through knowledge distillation to obtain a compressed model, and the compressed model is published to obtain an element recognition model.
  • the training model When the training model is obtained, it can be clearly detected that the parameters of the training model are huge, and the training of the model takes a long time, which easily causes the training model to fail to meet the real-time needs of intelligent customer service. Therefore, it is necessary to train the model. Under the premise of not losing the effect of the model as much as possible, compress the parameters of the model as much as possible, that is, the training model can be compressed through knowledge distillation, and the compressed training model can be used as the compression model, and the compressed model will be released. In order to obtain the feature recognition model.
  • the training model is compressed and released through knowledge distillation to obtain the element recognition model, so as to avoid too many parameters of the training model, which leads to the phenomenon that the training model determines the target problem element for a long time, and ensures the element recognition model Practicality.
  • a fourth embodiment of the user intention recognition method of this application is proposed.
  • This embodiment is step S20 of the first embodiment of this application, and each The historical elements corresponding to the historical questions, and the process of checking the duplicates of each historical element to obtain multiple relevant historical elements, include:
  • Step e Obtain the historical elements corresponding to each of the historical questions, perform duplicate checking processing on each of the historical elements, and determine whether there are duplicate historical elements in each of the historical elements;
  • step f if it does not exist, use each of the historical elements as related historical elements.
  • each historical element can be directly used as the relevant historical element corresponding to the target question.
  • each historical element is checked for duplicate processing to determine whether there is a repeated historical element. If it does not exist, each historical element is taken as a relevant historical element, thereby ensuring the high quality of the obtained relevant historical element.
  • the method includes:
  • Step h if it exists, determine whether the types of the repeated historical elements are the same;
  • each historical element it is determined whether the types of each repeated historical element (such as word meaning, pinyin, etc.) are the same, that is, when it is determined that there are duplicate historical elements, determine whether all historical elements are repeated If they are the same, you can directly select one of these repeated historical elements as the relevant historical element. If they are not the same, you need to filter for different types of repeated historical elements.
  • types of each repeated historical element such as word meaning, pinyin, etc.
  • step g if they are not the same, select one of the repeated historical elements of the same type as the demand historical element, and use the non-repetitive historical elements and the demand historical elements in each of the historical elements as related Historical elements, wherein each of the historical elements includes non-repetitive historical elements and repeated historical elements.
  • each historical element When the type of each historical element is found to be different after judgment, one of the repeated historical elements of the same type is randomly selected as the demand historical element, and the non-repetitive historical elements and each demand historical element in each historical element are regarded as Relevant historical elements corresponding to the target question.
  • the historical elements are either repetitive historical elements or non-repetitive historical elements.
  • each repeated historical element by determining whether the types of each repeated historical element are the same, if they are not the same, it is necessary to obtain a repeated historical element and a non-repetitive historical element together as the related historical element in each type of repeated historical element to ensure that The degree of conciseness of the relevant historical elements obtained.
  • an embodiment of the present application also proposes a user intention recognition device, and the user intention recognition device includes:
  • the first obtaining module is configured to receive the target question input by the user, and input the target question into the element recognition model to obtain a plurality of historical questions associated with the target question;
  • the second acquisition module is configured to acquire historical elements corresponding to each of the historical questions, and perform duplicate checking processing on each of the historical elements to obtain multiple related historical elements;
  • the prediction module is used to sequentially combine each of the relevant historical elements and the target question to obtain multiple target data, and predict each of the target data in turn through the element recognition model to determine each of the target data Whether the target data meets the preset requirements;
  • the determining module is configured to use the relevant historical elements in the target data that meets the preset requirements as the target question elements corresponding to the target question, and determine the user's intention based on the target question elements.
  • prediction module is also used for:
  • the first obtaining module is also used for:
  • the bert model completed after each classification training is used as the training model, and the element recognition model is determined based on the training model.
  • the first obtaining module is also used for:
  • the training questions currently traversed are classified and trained through a bert model until the traversal of each training problem is completed.
  • the first obtaining module is also used for:
  • the training model is compressed through knowledge distillation to obtain a compressed model, and the compressed model is published to obtain an element recognition model.
  • the second acquisition module is also used for:
  • the second acquisition module is also used for:
  • each of the historical elements includes non-repetitive historical elements and repeated historical elements.
  • each functional module of the user intention recognition device can refer to the various embodiments of the user intention recognition method of the present application, which will not be repeated here.
  • the present application also provides a user intention recognition device.
  • the user intention recognition device includes: a memory, a processor, and a user intention recognition program stored on the memory; the processor is used to execute the user intention recognition program to To achieve the following steps:
  • Each of the relevant historical elements and the target question are sequentially combined to obtain multiple target data, and each target data is sequentially predicted through the element recognition model to determine whether each target data meets the forecast Set requirements
  • the relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • the present application also provides a computer-readable storage medium, the computer-readable storage medium may be volatile or non-volatile, and the computer-readable storage medium stores one or more programs, The one or more programs may also be executed by one or more processors to implement the following steps:
  • Each of the relevant historical elements and the target question are sequentially combined to obtain multiple target data, and each target data is sequentially predicted through the element recognition model to determine whether each target data meets the forecast Set requirements
  • the relevant historical elements in the target data that meet the preset requirements are used as target question elements corresponding to the target question, and the user's intention is determined based on the target question elements.
  • the user intention recognition method provided by the present application further ensures the privacy and security of all the above-mentioned data
  • all the above-mentioned data can also be stored in a node of a blockchain.
  • target data and historical issues, etc. these data can be stored in the blockchain node.
  • the blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • 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) as described above. , Magnetic disks, optical disks), including 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.

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Abstract

La présente demande concerne le domaine technique du traitement de langage naturel. Sont divulgués un procédé, un appareil et un dispositif de reconnaissance d'intention d'utilisateur, ainsi qu'un support de stockage lisible par ordinateur. Le procédé comprend les étapes consistant à : recevoir une question cible entrée par un utilisateur, et entrer la question cible dans un modèle de reconnaissance d'élément de façon à acquérir une pluralité de questions historiques associées à la question cible ; acquérir des éléments historiques correspondant aux questions historiques, et effectuer un traitement de vérification en double sur les éléments historiques de façon à acquérir une pluralité d'éléments historiques pertinents ; combiner séquentiellement chaque élément historique pertinent avec la question cible de façon à acquérir une pluralité d'éléments de données cibles, et effectuer séquentiellement une prédiction sur chaque élément de données cibles au moyen du modèle de reconnaissance d'élément de façon à déterminer si chaque élément de données cibles satisfait une exigence prédéfinie ; et prendre des éléments historiques pertinents dans les données cibles qui satisfont l'exigence prédéfinie en tant qu'éléments de question cible correspondant à la question cible, et sur la base des éléments de question cible, déterminer l'intention de l'utilisateur. Le problème technique dans l'état de la technique, lié à la relativement faible précision lorsque des éléments sont reconnus, est résolu.
PCT/CN2020/122059 2020-02-21 2020-10-20 Procédé, appareil et dispositif de reconnaissance d'intention d'utilisateur, et support de stockage lisible par ordinateur WO2021164286A1 (fr)

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