WO2021068490A1 - Procédé et appareil de génération de message de réponse, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de génération de message de réponse, dispositif informatique et support de stockage Download PDF

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Publication number
WO2021068490A1
WO2021068490A1 PCT/CN2020/087818 CN2020087818W WO2021068490A1 WO 2021068490 A1 WO2021068490 A1 WO 2021068490A1 CN 2020087818 W CN2020087818 W CN 2020087818W WO 2021068490 A1 WO2021068490 A1 WO 2021068490A1
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Prior art keywords
message
target
user
phrase
synonymous
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PCT/CN2020/087818
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English (en)
Chinese (zh)
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满康瑞
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深圳壹账通智能科技有限公司
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063—Training
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/16—Speech classification or search using artificial neural networks
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/28—Constructional details of speech recognition systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/02—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/223—Execution procedure of a spoken command

Definitions

  • This application relates to the field of artificial intelligence technology, in particular to a method, device, computer equipment, and storage medium for generating a reply message.
  • customer service methods have gradually developed into online online customer service methods. Through the customer service system, communication and exchanges between merchants and customers can be facilitated.
  • the response mode of the customer service system can be divided into manual response mode and automatic response mode.
  • Sexual response, that is, the traditional automatic response mode has the problem of low information recognition accuracy.
  • a method for generating a reply message comprising:
  • a phrase is selected from each synonymous phrase set, and the selected phrases are combined to generate a reply message.
  • a reply message generating device comprising:
  • a user information obtaining module configured to receive voice messages input by a target user in real time, and obtain the identity of the target user according to the voice information;
  • the personality classification result acquisition module is used to acquire the relationship between the preset identity identifier and the personality classification result, and obtain the personality classification target corresponding to the identity identifier of the target user according to the relationship between the preset identity identifier and the personality classification result result;
  • Candidate message generating module for extracting target keywords contained in the voice message, and generating a response candidate message according to the target keywords and the personality classification target result;
  • a candidate message processing module configured to obtain the sentence structure of the reply candidate message, and split the reply candidate message according to the sentence structure to obtain multiple phrases;
  • the synonym acquisition module is used to acquire the synonym corresponding to each phrase from the preset thesaurus, and obtain the synonymous phrase set corresponding to each phrase;
  • the reply message generating module is used to select a phrase from each synonymous phrase set according to the sentence structure of the reply candidate message, and combine the selected phrases to generate a reply message.
  • a computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when the processor executes the computer program:
  • a phrase is selected from each synonymous phrase set, and the selected phrases are combined to generate a reply message.
  • a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:
  • a phrase is selected from each synonymous phrase set, and the selected phrases are combined to generate a reply message.
  • the foregoing reply message generation method, device, computer equipment and storage medium integrate the personality classification target results and the dimensions of the keywords in the voice message to generate reply candidate messages; on the other hand, the reply candidate messages are split and processed to obtain Multiple phrases, get the synonyms corresponding to each phrase, get the synonymous phrase set corresponding to each phrase, combine the phrases in each synonymous phrase set to generate a reply message, so that the final reply message obtained integrates multiple dimensional considerations.
  • the accuracy of information recognition can be improved, and the form of reply messages can be enriched, and the flexibility of reply can be improved.
  • Figure 1 is an application environment diagram of a method for generating a reply message in an embodiment
  • Figure 2 is a schematic flowchart of a method for generating a reply message in an embodiment
  • FIG. 3 is a schematic flowchart of a step of generating a reply message in an embodiment
  • Figure 4 is a structural block diagram of an apparatus for generating a reply message in an embodiment
  • Fig. 5 is an internal structure diagram of a computer device in an embodiment.
  • the method for generating a reply message provided in this application can be applied to the application environment as shown in FIG. 1.
  • the user terminal 102 communicates with the server 104 through the network.
  • the server 104 receives the voice message input by the target user from the user terminal 102 in real time, and obtains the identity of the target user according to the voice message.
  • the user terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers.
  • the server 104 may be implemented by an independent server or a server cluster composed of multiple servers.
  • a method for generating a reply message is provided. Taking the method applied to the server in FIG. 1 as an example for description, the method includes the following steps:
  • Step 202 Receive the voice message input by the target user in real time, and obtain the identity of the target user according to the voice information.
  • the voice message may be sent by a user through a user terminal for consulting on a business of interest.
  • the target user refers to a user who has a business consulting requirement and sends a voice message.
  • a user can download a financial application program on the user terminal, and then enter a financial voice message in the chat box loaded by the application program.
  • User identification is used to identify different users.
  • Step 204 Obtain the relationship between the preset identity identifier and the personality classification result, and obtain the personality classification target result corresponding to the identity identifier of the target user according to the relationship between the preset identity identifier and the personality classification result.
  • the preset relationship between the identity identifier and the personality classification result before obtaining the personality classification target result corresponding to the identity identifier of the target user, it also includes: sending a personality test question to the user terminal, and receiving feedback from different user terminals on the personality test question And the corresponding identity identification; according to the feedback on the personality test questions and the predetermined personality classification rules, the personality classification results corresponding to different identification identifications are obtained; according to the personality classification results corresponding to the different identification identifications, the preset identification identification and personality classification results are generated Relationship. For example, users can be divided into talkative and brief, extroverted and introverted, professional and non-professional, and so on.
  • Step 206 Extract the target keywords contained in the voice message, and generate response candidate messages according to the target keywords and the personality classification target results.
  • the voice message can be converted into a text message through automatic voice recognition technology, and the key words of the text message can be obtained through the trained natural language understanding model.
  • ASR Automatic Speech Recognition, automatic speech recognition
  • NLU Natural Language Understanding
  • models such as neural network models, use previous text data for training to classify input text data into various intents. If the voice message is a voice message, such as data from a phone or microphone, the original voice data is converted into text through automatic voice recognition. In addition, by analyzing the voice message, the emotional state of the user can be inferred. If it is a text message, the keywords are directly obtained through the trained natural language understanding model.
  • the preset database stores keywords and answers corresponding to the keywords. Search in the database according to the extracted keywords to obtain the answers corresponding to the extracted keywords; the comprehensive personality classification target results and the answers corresponding to the extracted keywords are generated Reply to candidate messages.
  • the answer candidate message includes the answer corresponding to the extracted keyword plus some emoticons, or the answer corresponding to the keyword is organized in a formal sentence as the answer candidate message.
  • Step 208 Obtain the sentence structure of the reply candidate message, and split the reply candidate message according to the sentence structure to obtain multiple phrases.
  • the reply candidate message Take “what is the current number of money owed to banks you are required to pay back?" as the reply candidate message, and obtain the sentence structure of the reply candidate message, such as subject-predicate structure, subject-predicate-object structure, subject-predicate-object structure, subject-predicate structure, subject The predicate-object-complement structure, etc., split the response candidate message according to the sentence structure, and obtain multiple different phrases such as what is the current number of, money owed to banks, you, are required to pay back, etc.
  • sentence structure of the reply candidate message such as subject-predicate structure, subject-predicate-object structure, subject-predicate-object structure, subject-predicate structure, subject The predicate-object-complement structure, etc.
  • Step 210 Obtain synonyms corresponding to each phrase from a preset thesaurus, and obtain a synonymous phrase set corresponding to each phrase.
  • search for replacement phrases/synonym phrases for each phrase from the preset corpus and get the synonymous phrase set corresponding to each phrase as ⁇ how many
  • Step 212 According to the sentence structure of the reply candidate message, a phrase is selected from each synonymous phrase set, and the selected phrases are merged to generate a reply message.
  • the reply candidate message is: "what is the current number of money owed to banks you are required to pay back?" to obtain the sentence structure of the reply candidate message, and split the reply candidate message according to the sentence structure, Obtain multiple different phrases such as what is the current number of, money owed to banks, you, are required to pay back, etc., and search for the replacement phrase/synonym phrase of each phrase from the preset corpus to obtain the synonymous phrase corresponding to each phrase Collection ⁇ how many
  • a phrase is selected from each synonymous phrase set, and the selected phrases are merged to generate a processed reply message with the same content as the reply candidate message but different wording. Specifically, it can be:
  • the above method for generating reply messages integrates multiple dimensions such as the user’s language model, personality classification target results, and keywords in the voice message to generate reply candidate messages; on the other hand, the reply candidate messages are split and processed to obtain multiple Get the synonym corresponding to each phrase, get the synonymous phrase set corresponding to each phrase, combine the phrases in each synonymous phrase set to generate a reply message, so that the final reply message not only integrates multiple dimensional considerations,
  • the accuracy of information recognition can be improved, and the form of reply messages can be enriched, and the flexibility of reply can be improved.
  • a phrase is selected from each synonymous phrase set, and the selected phrases are merged to generate the reply message before further including: 302.
  • Number each synonymous phrase set in sequence and determine the synonymous grammatical structure according to the current grammatical structure of the reply candidate message;
  • step 304 determine the synonymous grammatical structure according to the sequence of each synonymous phrase set in the synonymous grammatical structure Corresponding sentence structure;
  • select a phrase from each synonymous phrase set separately merge the selected phrases to generate a reply message, including: step 306, corresponding from the synonymous grammatical structure Select a sentence structure in the sentence structure, select a phrase from each synonymous phrase set to combine according to the selected sentence structure, perform grammatical correction on the combined sentence, and generate a reply message.
  • search for the replacement phrase/synonym phrase of each phrase from the preset corpus obtain the synonymous phrase set corresponding to each phrase, and number the synonymous phrase set in order to obtain: [1] ⁇ how many
  • obtain the current grammatical structure of the reply candidate message search in the grammar library according to the current grammatical structure of the reply candidate message, and obtain all the selectable synonymous grammatical structures of the reply candidate message, and determine the number of a single grammatical structure and synonymous phrase set
  • the sentence structure of the reply candidate message The details can be as follows: 1): "[1][2]you[3]"?
  • sentence structure 5 As an example, by selecting the synonymous phrase set 1_1, 2_2, 3_3, the sentence obtained is:
  • the reply message generation method further includes: performing language pattern recognition on the voice message based on the written language vocabulary to obtain the language pattern of the target user; generating the reply candidate message according to the target keywords and personality classification target results, including: The target user’s language model, personality classification target result, and preset demand classification model are used to obtain the user’s demand type.
  • the demand classification model is a neural network model that has been trained to solve the user’s demand type; Set the keyword-answer correspondence relationship to search to obtain the target answer corresponding to the target keyword; generate a response candidate message according to the user's demand type and the target answer. In this way, the response candidate message is generated from multiple dimensions to ensure its comprehensiveness.
  • the robot module in the server can be called to detect the written vocabulary contained in the voice message; when the written vocabulary contained in the voice message is less than a preset threshold, the user’s language mode is obtained as an informal language mode; when the voice message contains When the written language vocabulary of is greater than or equal to the preset threshold, the user's language mode is obtained as a formal language mode.
  • Pre-set rules If the number of written words in the voice message is less than the threshold, it is determined that the user language mode belongs to the informal language mode L1; if the number of written words in the voice message is greater than or equal to the threshold, it is determined that the user language mode belongs to the formal language mode L2.
  • the user's language mode can be determined. If the user uses spoken short sentences (such as "yo”, “hi”, "yup"), it is judged as an informal language mode. If the user uses the sentence structure of the written language, such as "good morning”, “good afternoon” and longer written sentence substructures, it is judged as a formal language mode.
  • a candidate reply message is generated. For example, a user habitually checks the bank balance at a certain time of the day. After learning this mode, the robot R can actively send data without the user's request.
  • the chatbot module of a formal role can suggest that the user set aside some money for long-term savings after receiving a monthly salary; chatbots in an informal role may also make similar suggestions. Over time, you can evaluate which role can more effectively achieve the user's financial goals.
  • the consumption can be presented as a pie chart, list or bar chart. If users prefer informal chatbots, these charts may be animated and cartoonized with "cute" artwork. However, if users prefer formal chatbots, then this representation is more likely to be concise, static, and commercial.
  • the training of the neural network model includes: obtaining sample data.
  • the sample data includes the language pattern of the sample user, the corresponding personality classification result and the corresponding demand type; the language pattern of the sample user and the corresponding personality classification result are input into the neural network model for processing Train to obtain the training result; compare the training result with the corresponding demand type of the sample user, adjust the parameters of the neural network model until the training result meets the preset conditions, and obtain the trained neural network model for solving the user demand type.
  • the preset database stores keywords and the answers corresponding to the keywords. According to the extracted target keywords, search in the database to obtain the answers corresponding to the extracted target keywords; comprehensive user’s needs types and the extracted target keywords correspond to Answer, generate reply candidate message. If the financial robot module is a chat robot model corresponding to the "official" role, it will respond with a lengthy formal sentence. If the financial robot module is a chat robot model corresponding to an "informal" role, a shorter colloquial sentence is used to respond, and emojis can also be used.
  • Robot R_f "Good morning Jacob, what can I do for you today?"
  • the robot module uses “formal” and “informal” chatbot roles, and there may be more and more personas, such as “happy”, “business”, “childish”, “scolding”, “old man” , “Wise”, “carefree” and so on.
  • a reply message generation device including: user information acquisition module 402, personality classification result acquisition module 404, candidate message generation module 406, candidate message processing module 408, synonym acquisition Module 410 and reply message generating module 412.
  • the user information obtaining module is used to receive the voice message input by the target user in real time, and obtain the identity of the target user according to the voice message.
  • the personality classification result acquisition module is used to acquire the relationship between the preset identity identifier and the personality classification result, and obtain the personality classification target result corresponding to the identity identifier of the target user according to the relationship between the preset identity identifier and the personality classification result.
  • the candidate message generating module is used to extract the target keywords contained in the voice message, and generate the reply candidate message according to the target keywords and the character classification target result.
  • the candidate message processing module is used to obtain the sentence structure of the reply candidate message, and split the reply candidate message according to the sentence structure to obtain multiple phrases.
  • the synonym acquisition module is used to acquire the synonyms corresponding to each phrase from the preset thesaurus, and obtain the synonymous phrase set corresponding to each phrase.
  • the reply message generation module is used to select a phrase from each synonymous phrase set according to the sentence structure of the reply candidate message, and combine the selected phrases to generate a reply message.
  • the reply message generation module further includes a sentence pattern determination module, which is used to sequentially number each synonymous phrase set, and determine the synonymous grammatical structure of the reply candidate message according to the current grammatical structure of the reply candidate message;
  • the sequence of the synonymous phrase set in the synonymous grammatical structure determines the sentence structure corresponding to the synonymous grammatical structure;
  • the reply message generation module is used to select a sentence structure from the syntactic structure corresponding to the synonymous grammatical structure. Select a phrase from each synonymous phrase set to combine the sentence structure of, and make grammatical corrections to the combined sentence to generate a reply message.
  • the personality classification result acquisition module also includes a corresponding relationship establishment module, which is used to send personality test questions to the user terminal, and receive feedback on the personality test questions from different user terminals and the corresponding identification; according to the personality test Question feedback and predetermined personality classification rules to obtain the personality classification results of different identities; according to the personality classification results corresponding to the different identities, the relationship between the preset identity identification and the personality classification results is generated.
  • a corresponding relationship establishment module which is used to send personality test questions to the user terminal, and receive feedback on the personality test questions from different user terminals and the corresponding identification; according to the personality test Question feedback and predetermined personality classification rules to obtain the personality classification results of different identities; according to the personality classification results corresponding to the different identities, the relationship between the preset identity identification and the personality classification results is generated.
  • the reply message generating device further includes a language pattern acquisition module, which is used to perform language pattern recognition on the voice message based on the written language vocabulary to obtain the language pattern of the target user; the candidate message generation module is used to generate the language pattern of the target user according to the language pattern of the target user.
  • a language pattern acquisition module which is used to perform language pattern recognition on the voice message based on the written language vocabulary to obtain the language pattern of the target user
  • the candidate message generation module is used to generate the language pattern of the target user according to the language pattern of the target user.
  • Personality classification target results and preset demand classification model to obtain the user's demand type, where the demand classification model is a neural network model that has been trained to solve the user's demand type; according to the target keyword in the preset keyword- Search in the answer correspondence relationship to obtain the target answer corresponding to the target keyword; generate a response candidate message according to the user's demand type and target answer.
  • the language mode acquisition module is also used to obtain the user’s language mode as an informal language mode when the written language vocabulary contained in the voice message is less than a preset threshold; when the written language vocabulary contained in the voice message is greater than or equal to the predetermined threshold, When the threshold is set, the user's language mode is obtained as the official language mode.
  • the candidate message generation module also includes a model building module before it is used to obtain sample data.
  • the sample data includes the language mode of the sample user, the corresponding personality classification result, and the corresponding demand type; the language mode of the sample user and The corresponding personality classification results are input to the neural network model for training, and the training results are obtained; the training results are compared with the corresponding types of needs of the sample users, and the parameters of the neural network model are adjusted until the training results meet the preset conditions, and the trained users are obtained.
  • Neural network model for solving the types of user needs.
  • Each module in the above reply message generating device can be implemented in whole or in part by software, hardware or a combination thereof.
  • the above modules can be embedded in the form of hardware or independent of the processor in the computer device, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 5.
  • the computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation 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, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the database of the computer equipment is used to store data such as the relationship between the identity identifier and the personality classification result, and reply messages.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating a reply message is realized.
  • FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the reply message generation method in any embodiment when the computer program is executed.
  • the method for generating a reply message mainly includes: receiving voice information input by a target user in real time, and obtaining the identity of the target user according to the voice information; obtaining the relationship between the preset identity and the result of personality classification, and obtaining the relationship between the preset identity and the personality classification result according to the prediction.
  • a candidate reply message is generated; the sentence structure of the candidate reply message is obtained, and the candidate reply message is split according to the sentence structure to obtain a plurality of phrases; each of the above-mentioned phrases is obtained from the preset thesaurus Synonyms corresponding to the phrase, obtain the synonymous phrase set corresponding to each phrase; according to the sentence structure of the reply candidate message, select a phrase from each synonymous phrase set, and combine the selected phrases To generate a reply message.
  • a computer-readable storage medium is provided.
  • the computer-readable storage medium may be non-volatile or volatile, and a computer program is stored thereon.
  • the method for generating a reply message mainly includes: receiving voice information input by a target user in real time, and obtaining the identity of the target user according to the voice information; obtaining the relationship between the preset identity and the result of personality classification, and obtaining the relationship between the preset identity and the personality classification result according to the prediction.
  • a candidate reply message is generated; the sentence structure of the candidate reply message is obtained, and the candidate reply message is split according to the sentence structure to obtain a plurality of phrases; each of the above-mentioned phrases is obtained from the preset thesaurus Synonyms corresponding to the phrase, obtain the synonymous phrase set corresponding to each phrase; according to the sentence structure of the reply candidate message, select a phrase from each synonymous phrase set, and combine the selected phrases To generate a reply message.
  • 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 may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

La présente invention concerne le domaine technique de l'intelligence artificielle, est appliquée à l'industrie financière, et concerne en particulier un procédé et un appareil de génération de message de réponse, un dispositif informatique et un support de stockage. Dans un mode de réalisation, le procédé comprend : la réception en temps réel d'un message vocal entré par un utilisateur cible, et l'acquisition d'une identification d'identité de l'utilisateur cible en fonction du message vocal; l'obtention d'un résultat cible de classification de personnalité en fonction d'une relation prédéfinie entre des identifications d'identité et des résultats de classification de personnalité; l'extraction d'un mot-clé cible contenu dans le message vocal, et la génération d'un message candidat de réponse selon le mot-clé cible et le résultat cible de classification de personnalité; l'acquisition de la structure de phrase du message candidat de réponse, la division du message candidat de réponse en fonction de la structure de phrase pour obtenir une pluralité de phrases, et l'acquisition à partir d'une bibliothèque de synonymes prédéfinie d'un synonyme correspondant à chaque phrase de façon à obtenir un ensemble de phrases synonymes correspondant à chaque phrase; et en fonction de la structure de phrase du message candidat de réponse, la sélection d'une phrase à partir de chaque ensemble de phrases synonymes et la combinaison des diverses phrases sélectionnées pour générer un message de réponse.
PCT/CN2020/087818 2019-10-12 2020-04-29 Procédé et appareil de génération de message de réponse, dispositif informatique et support de stockage WO2021068490A1 (fr)

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