WO2020147428A1 - Procédé et appareil de génération de contenu interactif, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de génération de contenu interactif, dispositif informatique et support de stockage Download PDF

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WO2020147428A1
WO2020147428A1 PCT/CN2019/120595 CN2019120595W WO2020147428A1 WO 2020147428 A1 WO2020147428 A1 WO 2020147428A1 CN 2019120595 W CN2019120595 W CN 2019120595W WO 2020147428 A1 WO2020147428 A1 WO 2020147428A1
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target
parameter
current
intention
information
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PCT/CN2019/120595
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Chinese (zh)
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柳明辉
徐国强
邱寒
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深圳壹账通智能科技有限公司
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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
    • 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
    • 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
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • 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

Definitions

  • This application relates to the technical field of semantic analysis, and in particular to a method, device, computer equipment and storage medium for generating interactive content.
  • chatbots focus on a single round of interaction with the user, so that the robot cannot obtain the true intention of the user well, because the important information of the user chat may be in the user's previous conversation.
  • This single-round-based chat mechanism ignores the current user’s previous rounds of chat topics and scene analysis, so the response content returned may be biased or even wrong. How to obtain chat content closer to the user's true intentions has become an urgent problem to be solved.
  • the embodiments of the present application provide a method, device, computer device, and storage medium for generating interactive content to solve the problem of how to obtain chat content that is closer to the user's real intention.
  • a method for generating interactive content including:
  • a preset reinforcement learning model to analyze at least one current intention parameter and at least one historical intention parameter to obtain a target intention, the target intention including at least one target parameter and parameter priority order;
  • the information conversion model is called to convert each retrieved text, obtain the current reply information corresponding to each retrieved text, and push at least one current reply information to the client according to the parameter priority order.
  • An interactive content generating device including:
  • the current information receiving module is configured to receive the current round information carrying the session identifier sent by the client, and obtain at least one current intention parameter based on the current round information;
  • a historical parameter obtaining module configured to query the session record database based on the session identifier, and obtain at least one historical intention parameter corresponding to the session identifier;
  • An acquiring target intention module configured to use a preset reinforcement learning model to analyze at least one current intention parameter and at least one historical intention parameter to acquire a target intention, the target intention including at least one target parameter and parameter priority order;
  • the retrieval text acquisition module is used to query and retrieve the text database based on each target parameter, and obtain the retrieval text corresponding to each target parameter;
  • the reply information obtaining module is used to call the information conversion model to convert each retrieved text, obtain the current reply information corresponding to each retrieved text, and push at least one current reply information to the client according to the parameter priority order.
  • a computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor.
  • the processor executes the computer-readable instructions, the following steps are implemented:
  • One or more readable storage media storing computer readable instructions
  • the computer readable storage medium storing computer readable instructions
  • the one Or multiple processors perform the following steps:
  • FIG. 1 is a schematic diagram of an application environment of a method for generating interactive content in an embodiment of the present application
  • Figure 2 is a flowchart of a method for generating interactive content in an embodiment of the present application
  • FIG. 3 is a schematic diagram of the realization process of obtaining the goal intention in an embodiment of the present application.
  • FIG. 4 is another flowchart of a method for generating interactive content in an embodiment of the present application.
  • FIG. 5 is another flowchart of a method for generating interactive content in an embodiment of the present application.
  • Fig. 6 is another flowchart of a method for generating interactive content in an embodiment of the present application.
  • FIG. 7 is another flowchart of a method for generating interactive content in an embodiment of the present application.
  • Fig. 8 is a schematic diagram of an interactive content generating apparatus in an embodiment of the present application.
  • Fig. 9 is a schematic diagram of a computer device in an embodiment of the present application.
  • the interactive content generation method provided by the embodiments of the present application can be applied in an application environment as shown in FIG. 1.
  • the interactive content generation method is applied in an interactive content generation system.
  • the interactive content generation system includes a client and a server. Communicate with the server through the network.
  • the client is also called the client, which refers to the program that corresponds to the server and provides local services to the client.
  • the client can be installed on, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices and other computer devices.
  • the server can be implemented by an independent server or a server cluster composed of multiple servers, which is used to accept the current round information sent by the user through the client and generate the reply information corresponding to the chat robot.
  • a method for generating interactive content is provided.
  • the method is applied to the server in FIG. 1 as an example for description, including the following steps:
  • the current round information is the information that the user enters into the client in the current round to express the user's intention. Further, the current round information may include intention information expressed in various forms, including but not limited to text data, voice information, or gesture action information, which is not limited here.
  • the session identifier is an identifier used to distinguish different session scenarios initiated by the server. Since the server can start several session scenarios with several clients, in order to distinguish each session scenario for session content analysis, the server needs to set a corresponding session identifier for each session scenario.
  • Intent is the purpose expressed by the user in the current round of information. It can be divided into “explicit intent” and “implicit intent”. The difference between the two is very obvious:
  • the so-called explicit intention means that a type of vocabulary expressing intention clearly appears in the user's words, such as: “hope”, “want”, “need” and so on.
  • this kind of display intention judgment is less difficult. It only needs to identify these fixed intention words, and then make connections with other components in the sentence. For example, if the user enters "I want to book a ticket to Beijing", the chat robot can recognize the intention word "want”, and get the intention parameters of the sentence to book the air ticket for me.
  • the air ticket itinerary includes the current location to Beijing.
  • Implicit intent is the opposite of explicit intent. There is no vocabulary that directly reflects the intent in the user's utterance. The chat robot needs to judge the user's intent based on text data. For chat bots, implicit intentions are more difficult to judge.
  • One of the more commonly used methods is to convert implicit intentions into explicit intentions before processing. For example, when a user expresses the implicit intention of "I am hungry", it is first transformed into the corresponding explicit intention form of "I want to eat”, and then the chatbot is processed according to the explicit intention.
  • the current intention parameter is the verbs and nouns (de-stop words) with actual meaning extracted from the text data after converting the current round information into corresponding text data, and express the user's intention in the most concise way.
  • stop words mainly include English characters, numbers, mathematical characters, punctuation marks, and function words with high frequency of use.
  • the text data is "Today's weather is really good”
  • the current intention parameters extracted from the text data include: today, weather and good (excluding the stop words "true” and "ah”).
  • step S10 the server can extract at least one current intention parameter through the current round information sent by the client, filter out the meaningless function words in the current round information, and directly obtain valid current intention parameters in the current round information , To prepare the technical basis for the subsequent combination of historical intention parameters to obtain the user’s true intention.
  • the session record database is a set of session records saved by the server based on each session scene (that is, each session identifier).
  • the historical intent parameters correspond to the current intent parameters, and are based on the intent parameters of all rounds saved before the current round of the session identified by the same session.
  • the server correspondingly records the current intention parameters generated by the current round of conversations in the conversation record database based on the same conversation identifier. Understandably, each current intent parameter is stored in the session record database to form a historical intent parameter, so that the subsequent server can match all corresponding historical intent parameters based on the same session identifier for real intent analysis.
  • step S20 the server can directly obtain all corresponding historical intent parameters in the session record database based on the session identifier, without the server re-extracting historical intent parameters in all historical sessions, and accelerate the processing speed of the server in analyzing the user's real intent.
  • the reinforcement learning model is preset on the server to analyze all historical intention parameters and current intention parameters input to the model, and obtain the analysis result as the target intention model.
  • the server may use a combination of an LSTM (Long Short-Term Memory) model and a softmax classifier as the reinforcement learning model.
  • LSTM Long Short-Term Memory
  • multi-level target intentions can be set for the chat scene.
  • the first-level target intention includes chat and questioning; the first-level target intention can continue to be divided into multiple second-level target intentions, and the chat intentions in the first-level target intention can be Continue to be divided into life, work and leisure; based on the second-level goal intention, it can be further divided into the third-level goal intention, and continue to be refined according to the needs of the scene.
  • the implementation process for the server to obtain the target intention through text data is as follows:
  • Preprocess the text data including removing punctuation marks from the corpus, removing stop words (words with no actual meaning), etc.
  • the word2vec tool is a NLP (Natural Language Processing, natural language processing) tool, which can vectorize all natural language words into a dense vector that the computer can understand (Dense Vector) for quantitative measurement The relationship between words, mining the relationship between words. Understandably, for similar words, their corresponding word vectors are also similar.
  • NLP Natural Language Processing, natural language processing
  • the LSTM model can solve the problem that the natural language processing statistical method can only consider the recent n words and ignore the words longer ago. It aims to find the correlation between words and words, increase the time text content in data analysis, and remember What happened before is then applied to the neural network to observe the connection with what happens next to the neural network to get the goal intention.
  • LSTM The characteristic of LSTM is that in addition to the RNN (Recurrent Neural Network) model, valve nodes of each layer are added, as shown in Figure 4. There are three types of valve nodes: forget gate, input gate and output gate. These valve nodes can be opened or closed, and are used to determine whether the memory state of the RNN model (the state of the previous network) in the previous output layer has reached the threshold, and thus is added to the calculation of the current layer.
  • RNN Recurrent Neural Network
  • the valve node uses the sigmoid function to calculate the calculation result of the previous output layer corresponding to the valve node in the RNN model; if the calculation result reaches the threshold, multiply the valve node output and the calculation result of the current layer as the input of the next layer ; If the threshold is not reached, forget the calculation result of the previous output layer corresponding to the valve node.
  • the memory function of the LSTM model is realized by these valve nodes.
  • the training results of the previous RNN model will be associated with the current RNN model for calculation, and when the valve node is closed, the previous calculation results will no longer affect the current calculation. Therefore, by adjusting the switch of the valve node, it is possible to realize the influence of the historical intention on the final acquisition of the target intention.
  • future intent analysis such as starting to analyze new paragraphs or chapters in natural language processing, just turn off the valve node.
  • the softmax classifier is introduced into the neural network to define a new output layer (target intention probability distribution).
  • the output layer of the LSTM model based on the neural network is not necessarily a probability distribution layer, so the softmax classifier can be introduced to the LSTM model at the end.
  • the softmax classifier can be used as an additional processing layer to take the output of the neural network (the final output of the LSTM model) Layer) becomes a probability distribution, that is, each output is a decimal between 0 and 1, and the sum of all output results is 1. For example, if the final output layer of the LSTM model is y1, y2, y3 and y4, the server can use the softmax classifier to process the final output layer of the LSTM model, using the following formula:
  • the value of y′ 1 y′ 2 y′ 3 y′ 4 can be calculated by the softmax classifier, where y′ i is the output probability of each target intention. Understandably, the target intention corresponding to the output with the largest value in y′ i is the target intention corresponding to the current round information.
  • the text data "I recently watched the new movie directed by Jiang Wen to suppress the evil”
  • the target intention obtained after recognition by the preset intention recognition model in step S20 is: the user discusses the movie, and the movie parameters Including: The movie is called "Xie Bu Zheng Zheng".
  • this embodiment can also set the parameter priority order for the target parameter, that is, set a higher parameter priority order for the noun corresponding to the specified action object after the verb, and set the secondary parameter priority order for the action executor .
  • the focus of people talking about events is the object of action execution, that is, the priority order of parameters of the object of action execution is generally higher than that of the person who performs the action.
  • Setting parameter priority order for different target parameters facilitates subsequent conceptual expansion based on different parameter priority order.
  • the server may also set the priority value of the parameter including the target intention with the largest number of target parameters to also be the highest.
  • the text data entered by the user through the client is "Query Longgang rental housing information, with an area of 20 square meters and a cheap price”.
  • the server can analyze that the target intention of the text data is to rent a house in Longgang, and split the text data for analysis:
  • the third group includes the most target parameters, that is, the parameter priority order of the third group is 3, and so on, the parameter priority order of the second group is 2, and the parameter priority order of the first group The order is 1.
  • the server responds based on the parameter priority order, it can search in descending order of the parameter priority order, that is, the third group of queried house rental results are first returned to the client.
  • the server obtains the realization process of the target parameter through the target intention:
  • the target parameters of the template can be obtained as "action person” and "leisure activity”. Extract the words corresponding to the above target parameters from the text data "I recently watched the new movie directed by Jiang Wen”: the action person corresponds to "I” (that is, the user), and the leisure activity "the movie does not suppress the evil” .
  • the parameter priority order corresponding to the actor is level 1
  • the parameter priority order corresponding to leisure activities is level 2.
  • step S30 the server can recognize the user's target intention through the preset reinforcement learning model, accurately determine the user's needs, and respond with the highest priority based on the parameter priority order corresponding to the target parameter, which will help to obtain the corresponding response based on the target parameter.
  • the reply message of the user accurately promotes the chat around the user’s focus to maintain the stickiness and practicality of the chat between the chat robot and the user.
  • the online database is an online search database, such as Baidu, Sogou, Google, or 360 search.
  • the search text is the reply text corresponding to the target parameter. For example, if the target parameter is "Today's Weather", the server can query the online weather through the network database to obtain the weather query result corresponding to "Today's Weather".
  • the weather query result is the search text.
  • the server may obtain the retrieval text corresponding to the target parameter through the online database, and provide the user with text information related to the current round information, thereby improving the accuracy of the reply content.
  • the information conversion model in this step is a model that converts text information into a form of reply information delivered to the user. For example, if the user inputs the current round information in the form of voice, the voice conversion model is used in this step to convert the retrieved text into voice information and output to the user, or it can be directly output to the client in text form according to the client's preferences.
  • the information conversion model is a model that mutually converts the meaning of text data and specific expressions, such as converting voice information into corresponding text data, and converting text data into corresponding sign language actions.
  • the information conversion model can be converted based on multiple expressions, so it includes multiple conversion models.
  • a voice conversion model that converts voice information into text information, such as RNN-HMM (Recurrent Neural Network-Hidden Markov Model, or Recurrent Neural Network-Hidden Markov) model or LSTM-HMM (Long Short-Term Memory), long and short term Memory network-Hidden Markov) model and other machine acoustic models, or gesture conversion models that convert gesture information into text information, such as FLDCRFs (Fuzzy based Latent dynamic Condition Random Fields) and other machine gesture recognition models .
  • RNN-HMM Recurrent Neural Network-Hidden Markov Model, or Recurrent Neural Network-Hidden Markov
  • LSTM-HMM Long Short-Term Memory
  • Memory network-Hidden Markov model Long Short-Term Memory
  • gesture conversion models that convert gesture information into text information, such as FLDCRFs (Fuzzy based Latent dynamic Condition Random Fields) and other machine gesture recognition models.
  • FLDCRFs Fuzzy based Latent dynamic Condition Random Fields
  • machine learning models such as the RNN-HMM machine acoustic model, the LSTM-HMM machine acoustic model, and the FLDCRFs machine gesture recognition model in this embodiment are well-known technologies, and will not be repeated here.
  • step S10 if the user inputs the current round information (voice information) through the microphone of the client terminal: "I recently watched the new movie by Jiang Wen Press positive".
  • the server obtains the current round information in the voice format collected by the microphone through the client, so it can determine that the current round information is voice information, and the RNN-HMM machine acoustic model can be used to convert the voice information into "I recently watched director Jiang Wen’s The text data of "new movie evil does not suppress the right”.
  • step S60 in order to maintain the consistency of the way of communicating with the user and improve the fun of the chat, the chat computer can also use the RNN-HMM machine acoustic model for the retrieved text obtained in step S50, and convert the retrieved text into voice information (current reply information) Return to the client.
  • the server may return the current reply information corresponding to the retrieved text to the client according to the priority order of the parameters, which is conducive to direct reply based on the user's focus, and promptly returns accurate current reply information for reference to the client to improve
  • the relevance of the chat content between the chat bot and the user the server can be set according to the needs of the scene so that the form of the output reply information is consistent with the way the user enters the current round of information, maintaining the consistency of chat habits, and improving the user’s and the chat Chatting is fun.
  • the server analyzes and obtains at least one current intention parameter by receiving the current round information sent by the client, and combines the current intention parameter with the session record database to obtain at least one historical intention parameter to obtain the target intention and Corresponding reply information can ensure that the chat bot generates accurate reply information to the client in a timely manner, avoiding inaccurate or irrelevant reply information only relying on the current intention parameters, and improving the interaction and reliability between the chat bot and the client .
  • receiving the current round information sent by the client includes:
  • the text data is the text information corresponding to the meaning of the current round information sent in a specific way.
  • the user sends a nodding action through the camera of the client as the current round information
  • the server can call the information conversion model (in this embodiment, the action The recognition model) converts the nodding action into text data "agree”.
  • step S11 the server converts the received current round information sent by the client into text data, which facilitates the server to perform further processing based on the text data to obtain the reply information corresponding to the current round information and return it to the client.
  • S12 Use the preset language processing model to analyze and process the text data to obtain at least one current intention parameter.
  • the preset language processing model of this embodiment can use word2vec, a NLP (Natural Language Processing, natural language processing) tool, which can vectorize all natural language words into dense vectors that can be understood by computers (Dense Vector), which is used to quantitatively measure the relationship between words and explore the relationship between words. Understandably, for similar words, their corresponding word vectors are also similar.
  • NLP Natural Language Processing, natural language processing
  • step S10 a preset language processing model is used to analyze and process the text data, and the realization process of obtaining at least one current intention parameter has been described in detail in step S10. In order to avoid repetition, it will not be repeated here.
  • the server may use a preset language processing model to parse the text data to obtain at least one current intention parameter, and may extract the most concise current intention expressed by the user in the current round, which is beneficial for the subsequent server to combine all historical intention parameters based on the current intention Get the real intention of the user.
  • the server converts the received current round information sent by the client into text data, which facilitates the server to further process the text data based on subsequent processing to obtain the reply information corresponding to the current round information and return it to the client end.
  • the server can use a preset language processing model to parse the text data to obtain at least one current intention parameter, and can extract the most concise current intention expressed by the user in the current round, which is conducive to the subsequent server to obtain the user’s truth based on the current intention and all historical intention parameters. intention.
  • step S30 using a preset reinforcement learning model to analyze at least one current intention parameter and at least one historical intention parameter to obtain the target intention, including:
  • the word vector is actually the semantic mapping matrix obtained by mapping the word to a semantic space.
  • the central word A maps the peripheral words BCDAEFG to obtain a parameter matrix W1
  • the central word L is mapped to the peripheral words BCDLEFG to obtain a parameter matrix W2. If the distance between W2 and W1 is similar, it means that A and L can be mapped to the same surrounding words, which may be synonyms.
  • Each word has a one-hot vector whose dimension is V. Among them, the one-hot vector is used to characterize that each element in the vector is associated with a word in the lexicon.
  • the vector of the specified word is expressed as: its corresponding element in the vector is set to 1, and the other elements are set to 0 (if The word has appeared in the vocabulary, then the corresponding position in the vocabulary in the vector is 1, and the other positions are all 0). If it does not appear in the vocabulary, the vector is all zeros.
  • the CBOW model continuous bag-of-words model, which takes the context of the word as input to predict the word itself
  • the implementation steps are as follows:
  • the size of batch_size must be an integer multiple of 2*window to ensure that each batch contains all samples corresponding to a word.
  • the neural network is iteratively trained for a certain number of times to obtain a parameter matrix from the input layer to the hidden layer with dimension N.
  • the transposition of each row in the matrix is the word vector matrix with the dimension V of the corresponding word.
  • the server may obtain the current word vector matrix and the historical word vector matrix corresponding to the current intent parameter and the historical intent parameter, respectively, to prepare a technical basis for the subsequent server to obtain the target intent based on the word vector matrix.
  • the combination of the LSTM (Long Short-Term Memory) model and the softmax classifier in step S30 can be used as the reinforcement learning model to analyze the current word vector matrix and the historical word vector matrix Analyze and process to obtain target intent.
  • LSTM Long Short-Term Memory
  • the current word vector matrix and the historical word vector matrix are input to the input valve of the preset reinforcement learning model.
  • the specific implementation process has been described in detail in the foregoing step S30. In order to avoid repetition, it will not be repeated here.
  • step S32 the server can recognize the user's target intention through the preset reinforcement learning model, accurately determine the user's needs, obtain chat content that is more suitable for the user's needs, and reduce the number of unanswered questions between the chat robot and the user.
  • the server may obtain the current word vector matrix and the historical word vector matrix corresponding to the current intent parameter and the historical intent parameter, respectively, to prepare a technical basis for the subsequent server to obtain the target intent based on the word vector matrix.
  • the server can identify the user's target intention through the preset reinforcement learning model, accurately determine the user's needs, obtain chat content that is more suitable for the user's needs, and reduce the number of unanswered questions between the chatbot and the user.
  • step 40 querying a networked database based on each target parameter to obtain the retrieval text corresponding to each target parameter specifically includes the following steps:
  • the attribute of the target parameter is a product attribute
  • the product attribute is a thing attribute marked by the content provider for each target parameter through the server.
  • the content provider is a provider that provides the server where the chat robot is located, and the provider can sell other physical products or provide various services at the same time.
  • the chatbot can recommend marketing products or services provided by the content provider at the right time when the user communicates, which is beneficial to the improvement of content providers using the chatbot system.
  • Product or service sales conversion rate For example, if the target parameter is "insurance”, if the insurance is a free product of the content provider, the content provider can mark "insurance" as a product attribute.
  • the server can associate the physical name or service name in each target intention template with the physical object or service type provided by the content provider itself, so that when the target parameter obtained by the server includes the physical name or service name.
  • Content providers can introduce corresponding products or services to users based on the association relationship.
  • the product database is a database that stores all the products or services that the content provider can provide.
  • Product recommendation information is an introduction to the product content of each product or service record in the product database.
  • the product recommendation information may include a product summary introduction and a detailed product introduction, so that the server first pushes the product summary introduction to the client.
  • the server receives the detailed product introduction request sent by the client, it indicates that the user is willing to continue to understand the details of the product, and then continue to send the detailed product introduction corresponding to the product to the client.
  • the server can pre-mark each target parameter with a label whether it belongs to the product attribute, which is helpful for the server to determine whether the target parameter is a product attribute immediately after obtaining the target parameter. time.
  • the server can obtain the product recommendation information corresponding to the target parameter based on the product database, and then push it to the user subsequently to improve the actual applicability of the chat robot and the scalability of chat content.
  • the interactive content generation method further includes: pushing the product recommendation information to the client in a preset product push format.
  • the preset product push format is a format preset by the server to push products to the client in the chat interface.
  • the server can set a list of product names to push, or a brief introduction to each product corresponding to the product.
  • the server when the server receives the product name selected by the client, it can associate the product name with the detailed product introduction established in step S51, so as to push the detailed product introduction to the client, so as to prevent users who are pushed to the client for the first time from feeling uncomfortable Detailed introduction of the product of interest.
  • the server can send product recommendation information to the client according to a preset product push format, so as to maintain the consistency of the format when the chat content involves products or services.
  • the online database is an online search database, such as Baidu, Sogou, Google, or 360 search.
  • the search text is the reply text corresponding to the target parameter. For example, if the target parameter is "Today's Weather", the server can query the online weather through the network database to obtain the weather query result corresponding to "Today's Weather".
  • the weather query result is the search text.
  • step S42 the server can obtain the retrieval text corresponding to the target parameter through the online database, and provide the user with the retrieval text related to the current round information, thereby improving the accuracy of the reply content; at the same time, the server does not need to store the retrieval corresponding to the target parameter locally Text can save the local storage space of the server.
  • the server can pre-mark whether each target parameter belongs to the product attribute label, which is helpful for the server to determine whether the target parameter is a product attribute, and it is not necessary to analyze whether each target parameter is a product attribute, which is beneficial to shorten the server The judgment time.
  • the server can obtain the product recommendation information corresponding to the target parameter based on the product database, and then push it to the user subsequently to improve the actual applicability of the chat robot and the scalability of chat content.
  • the server can obtain the retrieval text corresponding to the target parameter through the networked database, and provide the user with the retrieval text related to the current round information to improve the accuracy of the reply content; at the same time, the server does not need to store the retrieval text corresponding to the target parameter locally, which can save Local storage space of the server.
  • the interactive content generation further includes:
  • the user's personal information is static data and dynamic data collected by the server.
  • the static data is the personal information that the user actively enters when registering on the server and will not change for a long time, such as the user's region, age, gender, culture, Occupation and income, etc.
  • dynamic data is the data obtained by analyzing user behaviors during the server's interaction with users, such as living habits or consumption habits.
  • the following user personal information is needed:
  • Demographic attributes including basic information such as gender and age;
  • Equipment attributes terminal characteristics used, etc.
  • Behavior data user's behavior log data on the website such as access time and browsing path;
  • the user portrait is a highly refined label abstracted by the server based on the user's personal information, that is, the user characteristic identification.
  • tags By "sticking" tags to users, users can be described with highly general and easy-to-understand features, which facilitates further processing by the server based on tags (standardized information).
  • the implementation process of generating a user portrait based on the user's personal information is as follows:
  • Tag Value Exhaustive tag value
  • User portraits include tags such as gender, age, mobile phone brand, place of residence, and hobbies.
  • Xiao Ming is an example of a user portrait.
  • the output results of Xiao Ming's user portrait are: "Male”, “18-35”, “iPhone”, “Beijing” and "Football”.
  • the server may construct a user portrait based on the user's personal information, and prepare a technical basis for the subsequent server to analyze the user's personality based on the user portrait.
  • the user's personality is a person's stable attitude towards reality, and the personality characteristics shown in the habituated behavior pattern corresponding to this attitude.
  • Personality is relatively stable once it is formed, but it is not static, but plastic.
  • Personality is different from temperament and more embodies the social attributes of personality.
  • the core of personality differences between individuals is personality differences.
  • the shopping tags in the user portraits include the average browsing time and the average number of comparisons of the same type.
  • the online shopping tag records of three users are as follows:
  • the average browsing time is less than 10 minutes, and the average number of comparisons is less than 5 times.
  • the average browsing time is between 10 and 20 minutes, and the average number of comparisons is between 5 and 20.
  • Hesitant The average browsing time is greater than 20 minutes, and the average number of comparisons is greater than 20.
  • the server compares the shopping tags of the three users with the shopping personality reference template to get the following results:
  • User B always compares a small number of similar products (average number of comparisons is between 5 to 20 times) within an appropriate amount of time (average browsing time is between 10 minutes and 20 minutes) before placing an order, then user B's shopping personality is rational.
  • the server can also analyze the user portrait to obtain the user's chat personality.
  • the chat personality includes: excited, calm, or low. It needs to be added that the server can also determine the user's personality by analyzing the voice characteristics in the current round information input by the user in combination with the chat personality reference template.
  • the chat personality reference template for voice characteristics is as follows:
  • Hearty voice If the user is male, he has a capable personality and strong self-esteem; if the user is a female, he has a lively personality, good social skills, strong self-esteem, and lacks a sense of humor.
  • a mellow and loud voice This group of people is full of energy, regardless of whether the user is male or female.
  • the server may analyze the user portrait obtained in step S601 based on actual experience, obtain the user personality corresponding to the user, and prepare a technical basis for the subsequent server to adopt different reply modes based on different user characteristics.
  • the reply pattern database is a database that is pre-installed on the server and is composed of reply information patterns for different user personalities.
  • the target reply mode is a chat mode that responds to the user's personality. This embodiment is mainly applied to voice chat, so the reply mode is the tone reply mode when the chat robot chats with the user.
  • the two parties whose voices are synchronized during chat are more likely to be accepted by the other party. That is to say, if the user speaks slowly, and if the chat robot chats with the user's rhythm, it can increase the user's willingness to continue chatting with the chat robot. Based on this, the server can match the corresponding chat tone according to the user's personality to further enhance the chatting fun and emotional interaction between the user and the chat robot.
  • the server can configure a tone response mode for each user's personality, for example, the tone response mode for user personality such as enthusiasm, excitement, or positivity is a quick and enthusiastic tone response mode; for calm, plain or mature users
  • the tone response mode of the personality is a medium-speed and calm tone response mode.
  • the server may match the corresponding target reply pattern in the preset reply pattern library according to the user's personality, so as to further enhance the chat interest and emotional interaction between the user and the chat robot.
  • the server sends the target reply mode (may be a tone reply mode) obtained in step S603, such as a quick and enthusiastic tone reply mode, etc., to the information conversion model, so that the information conversion model converts the conceptual text into a corresponding target reply mode. Matching current reply message. Further, the information conversion model can obtain the current reply information that matches the target reply mode by adjusting the speech rate and the tone of the speech.
  • the target reply mode may be a tone reply mode obtained in step S603, such as a quick and enthusiastic tone reply mode, etc.
  • the server may combine the target reply mode and the information conversion model to obtain an updated information conversion model to adjust the tone that matches the target reply mode, and send the corresponding reply information to the client to enhance the communication between the chat robot and the user Emotional interactivity to improve the effectiveness of chat content.
  • the server can analyze the user portrait based on actual experience to obtain the user's corresponding personality, and match the corresponding target reply pattern in the preset reply pattern library according to the user's personality to further enhance the relationship between the user and the chat robot
  • the chat is interesting and emotional.
  • the server can send the adjusted current reply information that matches the target reply pattern to the client, enhancing the emotional interaction between the chat robot and the user, and improving the effectiveness of the chat content.
  • the server analyzes and obtains at least one current intention parameter by receiving the current round information sent by the client, and combines the current intention parameter with the session record database to obtain at least one historical intention parameter to obtain the target intention and Corresponding reply information can ensure that the chat bot generates accurate reply information to the client in a timely manner, avoiding inaccurate or irrelevant reply information only relying on the current intention parameters, and improving the interaction and reliability between the chat bot and the client .
  • the server converts the received current round information sent by the client into text data, which facilitates the server to perform further processing based on the text data to obtain the reply information corresponding to the current round information and return it to the client.
  • the server can use a preset language processing model to parse the text data to obtain at least one current intention parameter, and can extract the most concise current intention expressed by the user in the current round, which is conducive to the subsequent server to obtain the user’s truth based on the current intention and all historical intention parameters. intention.
  • the server may obtain the current word vector matrix and the historical word vector matrix corresponding to the current intent parameter and the historical intent parameter, respectively, to prepare a technical basis for the subsequent server to obtain the target intent based on the word vector matrix.
  • the server can identify the user's target intention through the preset reinforcement learning model, accurately determine the user's needs, obtain chat content that is more suitable for the user's needs, and reduce the number of unanswered questions between the chatbot and the user.
  • the server can pre-mark each target parameter with a label as to whether it belongs to the product attribute, so that the server can determine whether the target parameter is a product attribute immediately after obtaining the target parameter, and it is not necessary to analyze whether each target parameter is a product attribute, which helps shorten the server's determination time .
  • the server can obtain the product recommendation information corresponding to the target parameter based on the product database, and then push it to the user subsequently to improve the actual applicability of the chat robot and the scalability of chat content.
  • the server can obtain the retrieval text corresponding to the target parameter through the networked database, and provide the user with the retrieval text related to the current round information to improve the accuracy of the reply content; at the same time, the server does not need to store the retrieval text corresponding to the target parameter locally, which can save Local storage space of the server.
  • the server can analyze the user portrait based on actual experience to obtain the user personality corresponding to the user, and match the corresponding target reply pattern in the preset reply pattern library according to the user personality to further enhance the chat between the user and the chat robot Interesting and emotional interaction.
  • the server can send the adjusted current reply information that matches the target reply pattern to the client, enhancing the emotional interaction between the chat robot and the user, and improving the effectiveness of the chat content.
  • an interactive content generating device is provided, and the interactive content generating device corresponds to the interactive content generating method in the foregoing embodiment one-to-one.
  • the interactive content generating device includes a current information receiving module 10, a historical parameter obtaining module 20, a target intent obtaining module 30, a target intent obtaining module 30, a retrieval text obtaining module 40 and a reply information obtaining module 50.
  • the detailed description of each functional module is as follows:
  • the current information receiving module 10 is configured to receive the current round information carrying the session identifier sent by the client, and obtain at least one current intention parameter based on the current round information.
  • the historical parameter obtaining module 20 is configured to query the session record database based on the session identifier, and obtain at least one historical intention parameter corresponding to the session identifier.
  • the target intention acquisition module 30 is configured to analyze at least one current intention parameter and at least one historical intention parameter by using a preset reinforcement learning model to acquire a target intention.
  • the target intention includes at least one target parameter and parameter priority order.
  • the retrieval text obtaining module 40 is used for querying and retrieving the text database based on each target parameter, and obtaining the retrieval text corresponding to each target parameter.
  • the reply information obtaining module 50 is used to call the information conversion model to convert each retrieved text, obtain the current reply information corresponding to each retrieved text, and push at least one current reply information to the client according to the parameter priority order.
  • the receiving current information module 10 includes a round information receiving unit 11 and an intention parameter obtaining unit 12.
  • the round information receiving unit 11 is configured to receive the current round information sent by the client, call the information conversion model to identify the current round information, and obtain text data.
  • the intent parameter obtaining unit 12 is configured to analyze and process text data using a preset language processing model to obtain at least one current intent parameter.
  • the module for obtaining the target intention includes obtaining a current word vector matrix and a historical word vector matrix unit, obtaining a part of speech vector matrix and obtaining a target intention unit respectively.
  • the current word vector matrix and the historical word vector matrix unit are respectively obtained, and the current word vector matrix and the historical word vector matrix are respectively obtained based on at least one current intention parameter and at least one historical intention parameter.
  • part-of-speech vector matrix which is used for part-of-speech tagging based on current intent parameters and historical intent parameters, and obtain the current part-of-speech vector matrix and historical part-of-speech vector matrix corresponding to the current word vector matrix and the historical word vector matrix respectively.
  • the target intention unit is used to analyze the current word vector matrix and the historical word vector matrix using a preset reinforcement learning model to obtain the target intention.
  • the acquiring retrieval text module includes an acquiring product information unit and an acquiring retrieval text unit.
  • the acquiring product information unit is used for, if the attribute of the target parameter is a product attribute, query the product database corresponding to the product attribute based on each target parameter, and obtain the product recommendation information corresponding to each target parameter as the retrieval text.
  • the retrieval text unit is used to query the online database based on each target parameter if the attribute of the target parameter is not a product attribute, and obtain the retrieval text corresponding to each target parameter.
  • the interactive content generating device further includes a product information pushing module.
  • the product information push module is used to push product recommendation information to the client in a preset product push format.
  • the interactive content generating device further includes a product information pushing module and a question text obtaining module.
  • the product information push module is used to determine whether the search text contains at least two target options.
  • the obtaining question text module is used to obtain the question text corresponding to the at least two target options if it contains at least two target options, call the information conversion model to convert the question text, obtain the current reply information, and push the current reply information to Client.
  • Each module in the above interactive content generating device can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 9.
  • the computer device includes a processor, memory, network interface, and database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, computer-readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium.
  • the database of the computer equipment is used for data related to the interactive content generation method.
  • the network interface of the computer device is used to communicate with external terminals through a network connection.
  • the computer-readable instruction is executed by the processor to realize an interactive content generation method.
  • a computer device including a memory, a processor, and computer-readable instructions stored in the memory and capable of running on the processor.
  • the processor executes the computer-readable instructions to realize interactive content generation in the above-mentioned embodiments. Methods, such as S10 to S50 shown in FIG. 2.
  • the processor implements the functions of the modules/units of the interactive content generating apparatus in the foregoing embodiment when executing computer-readable instructions, for example, the functions of the modules 10 to 50 shown in FIG. 8. To avoid repetition, I won’t repeat them here.
  • one or more readable storage media storing computer readable instructions, the computer readable storage medium storing computer readable instructions, the computer readable instructions being executed by one or more processors
  • the one or more processors are executed to implement the interactive content generation method of the foregoing embodiment, for example, S10 to S50 shown in FIG. 2.
  • the computer-readable instruction is executed by the processor, the function of each module/unit in the interactive content generating apparatus in the above-mentioned apparatus embodiment is realized, for example, the function of the module 10 to the module 50 shown in FIG. 8. To avoid repetition, I won’t repeat them here.
  • the readable storage medium in this embodiment includes a nonvolatile readable storage medium and a volatile readable storage medium.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory can include random access memory (RAM) or external cache memory.
  • RAM random access memory
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain (Synchlink) DRAM
  • RDRAM direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

L'invention concerne un procédé et un appareil de génération de contenu interactif, ainsi qu'un dispositif informatique et un support de stockage, le procédé consistant à : recevoir des informations de partie actuelles contenant un identifiant de session envoyé par un terminal client et, sur la base des informations de partie actuelles, acquérir au moins un paramètre d'intention actuel (S10) ; sur la base de l'identifiant de session, interroger une base de données d'enregistrements de session pour acquérir au moins un paramètre d'intention historique correspondant à l'identifiant de session (S20) ; utiliser un modèle d'apprentissage par renforcement prédéfini pour analyser le ou les paramètres d'intention actuels dans le ou les paramètres d'intention historiques pour acquérir une intention cible, l'intention cible comprenant au moins un paramètre cible et un ordre de priorité de paramètre (S30) ; sur la base de chaque paramètre cible, interroger une base de données de texte de récupération pour acquérir un texte de récupération correspondant à chaque paramètre cible (S40) ; invoquer un modèle de conversion d'informations pour convertir chaque texte de récupération pour acquérir des informations de réponse actuelles correspondant à chaque texte de récupération et, sur la base de l'ordre de priorité de paramètre, envoyer les informations de réponse actuelles au terminal client (S50).
PCT/CN2019/120595 2019-01-18 2019-11-25 Procédé et appareil de génération de contenu interactif, dispositif informatique et support de stockage WO2020147428A1 (fr)

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