WO2020057014A1 - Procédé et appareil d'analyse et d'évaluation de dialogue, dispositif informatique et support de stockage - Google Patents

Procédé et appareil d'analyse et d'évaluation de dialogue, dispositif informatique et support de stockage Download PDF

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Publication number
WO2020057014A1
WO2020057014A1 PCT/CN2019/070103 CN2019070103W WO2020057014A1 WO 2020057014 A1 WO2020057014 A1 WO 2020057014A1 CN 2019070103 W CN2019070103 W CN 2019070103W WO 2020057014 A1 WO2020057014 A1 WO 2020057014A1
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dialogue
dialog
conversation
segment
evaluation
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PCT/CN2019/070103
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English (en)
Chinese (zh)
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闵锰锰
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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

Definitions

  • the present application relates to a method, a device, a computer device, and a storage medium for dialog analysis and evaluation.
  • a method, an apparatus, a computer device, and a storage medium for dialog analysis evaluation are provided.
  • a method of dialogue analysis and evaluation includes:
  • a device for dialogue analysis and evaluation includes:
  • An index module for determining a dialog evaluation index according to a preset big data sample library
  • An evaluation module for determining the quality evaluation of the effective dialogue segment according to the dialogue evaluation index and the effective dialogue segment
  • a recommendation module for generating corresponding dialogue suggestions based on valid dialogue fragments
  • the pushing module is configured to push the quality evaluation of the effective conversation segment and the corresponding conversation suggestion to the user terminal corresponding to the user identity.
  • a computer device includes a memory and one or more processors.
  • the memory stores computer-readable instructions.
  • the one or more processors are executed. The following steps:
  • One or more non-volatile computer-readable storage media storing computer-readable instructions.
  • the one or more processors execute the following steps:
  • FIG. 1 is an application scenario diagram of a method of dialog analysis and evaluation according to one or more embodiments.
  • FIG. 2 is a schematic flowchart of a method of dialog analysis and evaluation according to one or more embodiments.
  • FIG. 3 is a schematic diagram of a sub-process of step S204 in FIG. 2 according to one or more embodiments.
  • FIG. 4 is a schematic flowchart of steps before step S202 in FIG. 2 according to one or more embodiments.
  • FIG. 5 is a schematic diagram of a sub-process of step S202 in FIG. 2 according to one or more embodiments.
  • FIG. 6 is a schematic diagram of a sub-process of step S504 in FIG. 2 according to one or more embodiments.
  • FIG. 7 is a schematic diagram of a sub-process of step S208 in FIG. 2 according to one or more embodiments.
  • FIG. 8 is a schematic flowchart of a dialog analysis and evaluation method in another embodiment.
  • FIG. 9 is a block diagram of a device for dialog analysis and evaluation according to one or more embodiments.
  • FIG. 10 is a block diagram of a dialog analysis and evaluation apparatus according to another embodiment.
  • FIG. 11 is a block diagram of a computer device in accordance with one or more embodiments.
  • the method for dialog analysis and evaluation provided in this application can be applied to the application environment shown in FIG. 1.
  • the terminal 102 and the server 104 communicate through a network.
  • the server 104 determines a dialog evaluation index according to a preset big data sample database, obtains a valid dialog segment carrying a user identifier from the terminal 102, determines a quality evaluation of the valid dialog segment based on the dialog evaluation index and the valid dialog segment, and generates a corresponding response based on the valid dialog segment.
  • For the dialog suggestion push the quality evaluation of the effective conversation segment and the corresponding dialog suggestion to the terminal 102 corresponding to the user identification.
  • the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
  • the server 104 may be implemented by an independent server or a server cluster composed of multiple servers.
  • a method for dialogue analysis and evaluation is provided.
  • the method is applied to the server in FIG. 1 as an example, and includes the following steps:
  • S202 Determine a dialog evaluation index according to a preset big data sample database.
  • the big data sample database refers to a sample database for storing various types of data of the user's EQ and IQ.
  • the big data sample database includes the feedback results of the evaluation keywords, keyword dialog fragments, and keyword dialog fragments.
  • the dialog evaluation index refers to an evaluation index of a valid dialog segment, and the obtained valid dialog segment carrying a user identifier is evaluated according to the dialog evaluation index to determine the quality evaluation of the valid dialog segment.
  • various types of data in the big data sample database are processed, and the dialog evaluation index is determined according to the data processing results.
  • the effective dialogue fragment refers to a dialogue fragment including an evaluation keyword
  • the evaluation keyword refers to a keyword stored in a large data sample database, which is used to evaluate and test the user's emotional quotient and IQ.
  • the server obtains the conversation fragment carrying the user identification from the user's portable wearable device, and then processes the conversation fragment to obtain a valid conversation fragment carrying the user identification.
  • the process of obtaining a valid dialogue segment can be: converting the dialogue segment into text, matching the evaluation keywords in the big data sample database according to the converted text, and when the converted text exists corresponding to the evaluation keywords in the big data sample database When the text is displayed, the dialogue segment corresponding to the corresponding text is determined to be a valid dialogue segment.
  • S206 Determine the quality evaluation of the effective dialogue segment according to the dialogue evaluation index and the effective dialogue segment.
  • the quality evaluation includes the evaluation of the quality level of the dialogue and the evaluation of the type of the dialogue.
  • the evaluation of the quality level of the dialogue refers to the evaluation of the quality level of the dialogue.
  • the quality levels include excellent, good, and general.
  • Dialogue type evaluation refers to determining the type of dialogue according to the content of the dialogue, and the dialogue types include positive and negative categories.
  • the server generates corresponding dialogue suggestions according to the effective conversation fragments of the user.
  • the dialogue suggestions include generating corresponding recommendation questions based on the user's questions in the effective dialogue fragments, and generating corresponding recommended answers based on the user's answers in the user dialogue fragments.
  • the server determines the user according to the user identification carried on the valid conversation segment, and after determining the quality assessment of the effective conversation segment and generating the corresponding conversation suggestion, the server pushes the quality assessment of the effective conversation segment and the corresponding conversation suggestion to the user terminal corresponding to the user identity , The user can view the quality evaluation of the effective conversation fragments and corresponding conversation suggestions through the user terminal.
  • S204 includes:
  • the conversation fragment carrying the user's logo refers to the daily conversation fragment of the user, which can be obtained by the server from the user's portable wearable device.
  • the user's portable wearable device will acquire the daily conversation fragment of the user in real time and mark the user identification on it To the server.
  • the server After receiving the conversation fragment carrying the user's identity, the server converts the conversation fragment into text, and matches the evaluation keywords in the big data sample database according to the converted text.
  • the evaluation keywords refer to the key stored in the big data sample database. Words, which are used to evaluate and test the user's EQ and IQ.
  • the evaluation keywords can be set as required. When there is text in the converted text that matches the evaluation keywords in the big data sample database, it is determined that the dialogue segment is a valid dialogue segment carrying a user identifier.
  • the method before S202, the method includes:
  • S402 Collect keyword conversation fragments according to the evaluation keywords in the big data sample database
  • S406 Determine the feedback result of the dialogue according to the change of the intonation level and the change of the sound volume
  • S408 Record the keyword dialogue fragments and the feedback results of the dialogue into the big data sample database.
  • the evaluation keywords refer to the keywords stored in the big data sample database, which are used to evaluate and test the user's EQ and IQ.
  • the evaluation keywords can be set as needed.
  • the service provider can obtain information from common EQ and IQ. Extract the evaluation keywords from the test applet.
  • the server collects keyword dialogue fragments based on the evaluation keywords.
  • the keyword dialogue fragments refer to the dialogue fragments containing the evaluation keywords.
  • the server obtains the changes in the tone and volume of the two parties in the keyword conversation fragment, and determines the feedback result of the dialogue according to the changes in the tone and the volume.
  • the feedback result of the dialogue includes that the dialogue party is emotional or depressed because of the keyword dialogue segment.
  • an excessively high intonation or a loud voice indicates that the interlocutor's emotions are more agitated
  • a lower intonation or a lower voice indicates that the interlocutor's mood is lower.
  • S202 includes:
  • S504 Determine the conversation type and quality level of the recorded conversation segment according to the feedback result of the conversation;
  • S506 Establish a dialogue evaluation index by using a decision tree algorithm according to the dialogue type and the quality level of the dialogue.
  • Decision tree algorithm is a method to approximate the value of discrete functions. It is a typical classification method. It first processes the data, uses inductive algorithms to generate readable rules and decision trees, and then uses the decisions to analyze new data.
  • a decision tree is essentially a process of classifying data through a series of rules. The decision tree algorithm constructs a decision tree to discover the classification rules contained in the data. How to construct a high-precision, small-scale decision tree is the core content of a decision tree algorithm. Decision tree construction can be performed in two steps. The first step is the generation of a decision tree: the process of generating a decision tree from a training sample set.
  • the training sample data set is a data set that has a history and a certain degree of comprehensiveness according to actual needs and is used for data analysis and processing.
  • the second step is the pruning of the decision tree.
  • the pruning of the decision tree is a process of checking, correcting, and pruning the decision tree generated in the previous stage. It is mainly used in a new sample data set (called a test data set).
  • the preliminary rules generated during the data verification decision tree generation process will cut off the branches that affect the accuracy of the pre-balance.
  • the recorded dialogue fragments in the big data sample database refer to the keyword dialogue fragments entered according to the evaluation keywords, which are used to evaluate and test the user's emotional quotient and IQ.
  • the evaluation keywords can be set as required by themselves.
  • the service provider Extract evaluation keywords from common EQ and IQ test applets.
  • the feedback result of the dialogue refers to the feedback result of the keyword conversation segment, including the conversation party being emotionally excited or depressed because of the keyword conversation segment.
  • Dialogue types include positive and negative dialogues, and quality levels include excellent, good, and average.
  • a decision tree algorithm is mainly used to generate a decision tree, that is, a decision tree algorithm is used to process the recorded dialogue fragments and the feedback results of the dialogue according to the type of dialogue and the quality level of the dialogue, and the dialogue evaluation index is established using the induction algorithm , And then use the dialogue evaluation index to analyze the effective dialogue fragments.
  • S504 includes:
  • the recorded dialogue is divided into different quality levels, and the quality levels include excellent, good, and average.
  • the feedback results include that the conversation party is emotional or depressed because of the keyword conversation segment.
  • the conversation is determined as a negative conversation, and the quality level of the conversation is determined according to the degree of emotional depression.
  • the degree of the emotional depression of the interlocutor can be determined by the voice and volume of the interlocutor.
  • a conversation party is emotionally excited by a keyword conversation segment, first classify the conversation into a positive conversation or a negative conversation according to the content of the conversation, and then determine the quality level of the conversation according to the degree of emotional agitation.
  • the tone and volume of the party are determined. For example, first determine the tone and volume of the interlocutor during normal speaking, and then determine the degree of depression or emotional excitement of the interlocutor based on the tone and volume of the interlocutor in the conversation segment.
  • S208 includes:
  • S704 Generate a corresponding recommended answer according to the answer in the valid conversation segment.
  • the server obtains the keyword conversation fragments with the same keywords in the big data sample database according to the questions in the effective conversation fragments. According to the keyword conversation fragments with the excellent quality in the keyword conversation fragments with the same keywords, the server generates the effective conversation fragments. Ask the corresponding recommended question.
  • the server obtains the keyword conversation fragments with the same keywords in the big data sample database according to the answers in the effective conversation fragments. According to the keyword conversation fragments with the same quality in the keyword conversation fragments with the same keywords, the server generates the effective conversation fragments. The corresponding recommended answer.
  • the method includes:
  • S802 Determine the quality level of the effective dialogue segment according to the quality evaluation of the effective dialogue segment, and the quality level includes excellent, good, and average;
  • the server determines the quality level of the effective dialogue segment according to the quality evaluation of the effective dialogue segment, selects the effective dialogue segment with the excellent quality level from the effective dialogue segment, and updates the big data sample database based on the effective dialogue segment with the excellent quality level, that is, the quality
  • the effective and effective dialogue fragments with the rank of excellent are stored in the big data sample database.
  • a decision tree algorithm is used to update the dialogue evaluation index.
  • a decision tree algorithm is used to update the dialogue evaluation index, which is the pruning process of the decision tree.
  • the newly entered quality level in the updated big data sample database is an excellent effective dialogue segment.
  • the dialogue evaluation index is We will update and continuously improve the dialogue evaluation index.
  • steps in the flowchart of FIG. 2-8 are sequentially displayed in accordance with the directions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated in this document, the execution of these steps is not strictly limited, and these steps can be performed in other orders. Moreover, at least a part of the steps in FIG. 2-8 may include multiple sub-steps or stages. These sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. These sub-steps or stages The execution order of is not necessarily performed sequentially, but may be performed in turn or alternately with at least a part of another step or a sub-step or stage of another step.
  • a dialogue analysis and evaluation device including: an index module 902, an acquisition module 904, an evaluation module 906, a recommendation module 908, and a push module 910, where:
  • An index module 902 configured to determine a dialog evaluation index according to a preset big data sample database
  • An obtaining module 904 configured to obtain a valid conversation segment carrying a user identifier
  • An evaluation module 906, configured to determine the quality evaluation of the effective dialogue segment according to the dialogue evaluation index and the effective dialogue segment;
  • a recommendation module 908, configured to generate a corresponding dialogue suggestion according to a valid dialogue segment
  • the pushing module 910 is configured to push the quality evaluation of the effective conversation segment and the corresponding conversation suggestion to a user terminal corresponding to the user identifier.
  • the obtaining module 904 includes a matching module 912.
  • the matching module 912 is configured to obtain a conversation segment carrying a user identifier, convert the conversation segment into text, and match the big data sample according to the converted text.
  • the evaluation keywords in the database When there is text in the converted text that matches the evaluation keywords in the big data sample database, it is determined that the conversation segment is a valid conversation segment carrying a user identification.
  • the dialogue analysis and evaluation device includes an entry module 914.
  • the entry module 914 is configured to collect keyword conversation fragments according to the evaluation keywords in the big data sample database, and obtain the keyword conversation fragments according to the keyword conversation fragments.
  • the changes in intonation and volume of both sides of the dialogue are determined according to the changes in intonation and volume, and the feedback results of the dialogue are determined.
  • the keyword dialogue fragments and the feedback results of the dialogue are entered into the big data sample database.
  • the index module 902 includes a processing module 916, and the processing module 916 is configured to obtain the recorded dialogue fragments and the feedback result of the dialogue in the big data sample library, and determine the feedback result based on the dialogue.
  • the dialogue type and quality level of the recorded dialogue fragments are based on the dialogue type and the quality level of the dialogue using a decision tree algorithm to establish a dialogue evaluation index.
  • the processing module 916 includes a dividing module 918.
  • the dividing module 918 is configured to divide the recorded dialogue into a positive dialogue and a negative dialogue according to the feedback result, and record the entered dialogue according to the feedback result. Divided into different quality levels, quality levels include excellent, good and average.
  • the recommendation module 908 includes a question-and-answer module 920.
  • the question-and-answer module 920 is configured to generate corresponding recommended questions based on the questions in the valid conversation segment, and generate corresponding responses based on the answers in the valid conversation segment. Recommended answer.
  • the dialogue analysis and evaluation device includes an update module 922.
  • the update module 922 is configured to determine the quality level of the effective dialogue segment according to the quality evaluation of the effective dialogue segment, and the quality level includes excellent, good
  • the big data sample database is updated according to the effective conversation segments with excellent quality levels
  • the dialogue evaluation index is updated according to the updated big data sample database.
  • Each module in the above-mentioned dialog analysis and evaluation device may be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the hardware form or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor calls and performs the operations corresponding to the above modules.
  • a computer device is provided.
  • the computer device may be a server, and the internal structure diagram may be as shown in FIG. 11.
  • the computer device includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile computer-readable storage medium and an internal memory.
  • the non-volatile computer-readable storage medium stores an operating system, computer-readable instructions, and a database.
  • This internal memory provides an environment for the operating system and computer-readable instructions in a non-volatile computer-readable storage medium.
  • the database of the computer device is used to store keyword dialog fragment data and dialog feedback result data.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer-readable instructions are executed by a processor to implement a method of dialog analysis and evaluation.
  • FIG. 11 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied.
  • the specific computer equipment may be Include more or fewer parts than shown in the figure, or combine certain parts, or have a different arrangement of parts.
  • a computer device includes a memory and one or more processors.
  • Computer-readable instructions are stored in the memory.
  • the one or more processors execute the following steps:
  • One or more non-volatile computer-readable storage media storing computer-readable instructions.
  • the one or more processors execute the following steps:
  • 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 is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM dual data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Synchlink DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

L'invention concerne un procédé d'analyse et d'évaluation de dialogue, comprenant les étapes consistant à : déterminer un indice d'évaluation de dialogue en fonction d'une base de données d'échantillons de mégadonnées prédéfinie ; obtenir un segment de dialogue effectif portant une identification d'utilisateur ; déterminer une évaluation de la qualité du segment de dialogue effectif en fonction de l'indice d'évaluation de dialogue et du segment de dialogue effectif ; générer une suggestion de dialogue correspondante en fonction du segment de dialogue effectif ; et transmettre l'évaluation de la qualité du segment de dialogue effectif et la suggestion de dialogue correspondante à un terminal utilisateur correspondant à l'identification d'utilisateur.
PCT/CN2019/070103 2018-09-18 2019-01-02 Procédé et appareil d'analyse et d'évaluation de dialogue, dispositif informatique et support de stockage WO2020057014A1 (fr)

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