WO2021042902A1 - Procédé d'identification d'intention d'utilisateur dans un dialogue à plusieurs cycles, et dispositif associé - Google Patents

Procédé d'identification d'intention d'utilisateur dans un dialogue à plusieurs cycles, et dispositif associé Download PDF

Info

Publication number
WO2021042902A1
WO2021042902A1 PCT/CN2020/103922 CN2020103922W WO2021042902A1 WO 2021042902 A1 WO2021042902 A1 WO 2021042902A1 CN 2020103922 W CN2020103922 W CN 2020103922W WO 2021042902 A1 WO2021042902 A1 WO 2021042902A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
dialogue
user
dialogue state
state
Prior art date
Application number
PCT/CN2020/103922
Other languages
English (en)
Chinese (zh)
Inventor
陈涛
张毅
Original Assignee
深圳Tcl数字技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳Tcl数字技术有限公司 filed Critical 深圳Tcl数字技术有限公司
Publication of WO2021042902A1 publication Critical patent/WO2021042902A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation

Definitions

  • the present disclosure relates to the field of voice interaction technology, and in particular to a method and related equipment for recognizing user intentions in multiple rounds of dialogue.
  • the natural language analysis technology in the prior art is generally data-driven and based on machine learning.
  • the dialogue technology based on natural language analysis is divided into single-round dialogue and multi-round dialogue.
  • Multi-round dialogue is a way in which the user's intention is initially clarified in a human-machine dialogue, and then necessary information is obtained to finally obtain a clear user instruction. Multiple rounds of dialogue correspond to the handling of one thing.
  • the multi-round dialogue system has modules such as language understanding, language generation, dialogue management, and knowledge base.
  • Dialogue management also includes state tracking and action selection sub-modules. It can be considered that a multi-round dialogue system is an extension of a single-round dialogue based on analysis. In each round of dialogue, the semantics of the speech is understood and internal representations are generated. Dialogue management uses a finite state machine, which represents the entire process of obtaining information in a dialogue. After several rounds of dialogue, the system gradually obtains the required information and performs tasks.
  • the existing multiple rounds of dialogue in the prior art are based on the previous round of dialogue search for query matching in the next round of dialogue.
  • the user’s previous round of dialogue is: "Piggie Pig”
  • the next round of dialogue is "horrible”
  • search for information related to "horror” on Peggy Little Pig's search results because there is a gap between these two words.
  • the final analysis of the user’s intention is inaccurate, and the inaccurate analysis of the user’s intention leads to the inaccurate return of the system behavior, which leads to unsmooth communication between the user and the dialog device, or leads to the dialog device
  • the instruction execution error directly brings inconvenience to the user's use of the dialogue device.
  • the present disclosure provides a method and related equipment for recognizing user intentions in multiple rounds of dialogue, which overcomes the lack of consideration of the relationship between the previous and subsequent dialogues in the multiple rounds of dialogue in the prior art.
  • the query matching of the next round of dialogue is always performed on the basis of the previous round of dialogue search, which leads to the defect that the accuracy of the subsequent round of dialogue query matching is low.
  • this embodiment discloses a method for recognizing user intentions in multiple rounds of dialogue, which includes the following steps:
  • the user intention is recognized according to the following information, and the user intention recognition result is obtained.
  • the method before the step of acquiring the first dialogue state of the above information and the second dialogue state of the following information in the multiple rounds of dialogue, the method further includes:
  • the step of separately acquiring the first dialogue state of the above information and the second dialogue state of the following information in the multiple rounds of dialogues includes:
  • the user's intention is identified according to the following information to obtain the user Intent identification results, including:
  • the correlation between the first dialogue state and the second dialogue state is greater than or equal to a preset first threshold, acquiring system feedback information corresponding to the first dialogue state;
  • the user's intention is identified according to the following information, and the user's intention identification result is obtained.
  • the user's intention is identified according to the following information to obtain the user Before the step of intent to identify the result, it also includes:
  • the step of calculating the first correlation according to the first slot information and the second slot information includes:
  • the first correlation is calculated according to the size of the edit distance.
  • the user intention is identified according to the following information, and the user intention recognition result is obtained Before the steps, it also includes:
  • the step of calculating the second correlation according to the third slot information and the second slot information includes:
  • the second correlation is calculated according to the size of the edit distance.
  • the method further includes:
  • the method further includes:
  • the step of recognizing the user's intention based on the following information to obtain the user's intention recognition result includes:
  • the identification method further includes:
  • the first correlation between the first dialogue state and the second dialogue state is greater than or equal to the preset first threshold, then the first dialogue state, the system feedback information and the following information are combined to the user Intentions are recognized, and the results of user intent recognition are obtained.
  • the identification method further includes:
  • the correlation between the system feedback information and the second dialogue state is greater than or equal to the preset second threshold, the first dialogue state, the system feedback information and the following information are combined to identify the user's intention , Get the result of user intention recognition.
  • the step of combining the first dialogue state, the system feedback information and the following information to identify the user's intention, and obtaining the user's intention recognition result includes:
  • this embodiment also discloses a computer device, including a memory and a processor, the memory stores a computer program, wherein the steps of the method are implemented when the processor executes the computer program.
  • this embodiment also discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to implement the steps of the method.
  • the difference between the first dialogue state and the second dialogue state is calculated First correlation, and compare the first correlation with a preset first threshold, and if the first correlation is less than the preset first threshold, only identify the user's intention based on the following information to obtain User intention recognition result. Since this embodiment fully considers the relevance of the interactive information between the front and rear dialogues, when there is a large difference in the interactive information between the two, the latter dialogue will be used as a separate information for user intent analysis, so that more information can be obtained. It provides a basis for accurate analysis results and accurate feedback for users' information.
  • FIG. 1 is a flowchart of steps of a method for recognizing user intentions in multiple rounds of dialogue in an embodiment of the present disclosure
  • Figure 2 is a schematic diagram of the information flow of the multi-round dialogue system
  • Figure 3 Schematic diagram of the principle structure of a multi-round dialogue system
  • Fig. 4 is a schematic diagram of a framework of an exemplary application scenario in an embodiment of the present disclosure
  • Fig. 5 is a block diagram of the principle structure of a computer device in an embodiment of the present disclosure.
  • Multi-round dialogue is the process of human-computer interaction. After the user’s intention is initially clarified, the necessary information is obtained to finally obtain a definite user instruction. Multi-round dialogue corresponds to the processing of one thing, which can be expressed as a multitude of interactions between humans and machines. If the user's instructions can be clearly defined in one dialogue, then multiple rounds of dialogue can be expressed as one dialogue interaction between man and machine.
  • the current multi-round dialogue ignores the randomness between the utterances, and it is necessary to judge whether it is necessary to make a decision based on the previous dialogue context based on the relevance of the content before and after the dialogue, and there may be between the content before and after the dialogue. Irrelevant situations. If the content between the front and the back is not relevant, when the system feedback behavior is generated for the next round of content, the retrieval is implemented on the basis of the previous round of content, which may lead to the subsequent round of information The resulting feedback behavior is inaccurate.
  • This embodiment discloses a method for identifying user intentions in multiple rounds of dialogue. By analyzing the relevance of information between the front and back rounds of dialogue, it is determined whether it is necessary to perform the next round of dialogue information on the basis of the query results of the previous round of dialogue. For example, when the user says “Little Pig” and then "Terror", first judge the relevance of the two. If they are not relevant, perform a single-round search, that is, search for "Little Pig" and “Single” Search for "horror”. If relevant, search for "Little Pig Peggy” first, and then search for "horror” on the search results of "Little Pig Peggy". Since there is no correlation between "Little Pig Peggy" and "Terror", better and more accurate results can be obtained by using the method disclosed in this embodiment.
  • the method may include the following steps, for example:
  • Step S101 Acquire the first dialogue state of the above information and the second dialogue state of the following information in multiple rounds of dialogue.
  • the first dialogue state corresponding to the above information and the second dialogue state of the following information are obtained respectively.
  • the following information is the voice information sent by the user during the next human-computer interaction during the human-computer interaction in multiple rounds of dialogue.
  • the above information is the human-computer interaction in the multiple rounds of dialogue.
  • the user The previous voice message.
  • the above information and the following information belong to the previous and next voice information sent by the user, and the above information and the following information belong to the natural voice dialogue. You can use Chinese, English or other natural voices to conduct multiple rounds of conversations with the dialogue system. .
  • the dialogue system receives the voice information sent by the user, it helps the user complete a task, which is usually the task of accessing information.
  • the dialogue state includes the text converted from the voice information sent by the user and the information related to the text information analyzed according to the text. After obtaining the above information and the following information, the above information and the following information are respectively subjected to voice recognition and semantic recognition to obtain the first dialogue state of the above information and the second dialogue state of the following information.
  • the multiple rounds of dialogue include: voice understanding, voice generation, dialogue management, knowledge base search and other steps.
  • Dialogue management also includes steps such as dialogue state tracking and action selection. It can be considered that multiple rounds of dialogue are the expansion of a single round of dialogue based on analysis. In each round of dialogue, the semantics of the speech is understood and internal representations are generated. Dialogue management uses a finite state machine, which represents the entire process of obtaining information in a dialogue. After several rounds of dialogue, the system gradually obtains the required information and performs tasks such as flight information query.
  • the above information sent by the user is first obtained, and the voice recognition result is generated through the voice recognition of the above information sent by the user, which is the text information corresponding to the above information; the semantic analysis module maps the text information to the user
  • the dialogue state is the first dialogue state; similarly, voice recognition is performed on the following information sent by the user to obtain the voice recognition result, and the voice recognition result is mapped to the user dialogue state to obtain the second dialogue state.
  • this step in order to obtain the first dialogue state of the above information, it is first necessary to perform voice recognition on the above information, identify the text information contained in the above information, and then perform semantic analysis on the recognized text information. Get the information contained in the text message.
  • semantic analysis there are generally two processing methods. One is to retrieve information corresponding to the text information, and the other is to generate information corresponding to the text information based on a generation method.
  • the way to retrieve the information corresponding to the text information generally requires the establishment of a storage database, in which a large amount of dialogue data is stored, and an index is established between the dialogue data and the dialogue keywords. After the contained keywords are identified, the corresponding dialogue data in the database is output according to the keywords, that is, the analyzed first dialogue state corresponding to the text information.
  • the semantic analysis processing module uses a large amount of data to construct a speech analysis model.
  • the voice analysis model After the user inputs a piece of text information, the voice analysis model outputs an analysis result corresponding to the text information.
  • the voice analysis model is constructed on the basis of a large amount of dialogue data based on a deep learning neural network.
  • the result of the analysis of the voice analysis model is the dialogue state corresponding to the above information or the following information.
  • Step S102 If the first correlation between the first dialogue state and the second dialogue state is less than a preset first threshold, the user intention is recognized according to the following information, and the user intention recognition result is obtained.
  • the first correlation between the first dialogue state and the second dialogue state is calculated, and it is determined whether the first correlation is It is less than the preset first threshold. If it is less than, it is determined that the correlation between the above information and the following information is small, and the user's intention can be directly identified based on the following information to obtain the user's intention recognition result.
  • the first correlation steps include:
  • the first correlation is calculated according to the first slot information and the second slot information.
  • the slot information is the information needed to transform the preliminary user's intentions into clear user instructions in the process of multiple rounds of dialogue, and one slot corresponds to a type of information that needs to be acquired in the processing of one thing.
  • Slot information is a kind of information that must be obtained. It does not need to be completely filled in multiple rounds of dialogue. It is divided into required slot information and non-required slot information. Since the non-mandatory slot information can be obtained based on the context information, it can exist in the form of a default value.
  • the weather conditions must be searched based on geographic location.
  • the user's location can be known as: Beijing
  • the default query corresponding to the dialog can be defaulted The weather in Beijing, so the system can directly give feedback: search for the weather in Beijing.
  • the step of calculating the first correlation according to the first slot information and the second slot information includes:
  • the first correlation is calculated according to the size of the edit distance.
  • the round information is combined with the input to determine the query status of the round, and the user status in the previous round is determined as: currency information query, according to the character strings "RMB” and “USD” corresponding to the two slots of the information below , And the slot information string corresponding to the last round of system feedback information: "currency information query”, calculate the edit distance between the two, and get the string information corresponding to the following information converted into the corresponding information above The minimum number of editing operations required for the string to get the correlation between the two.
  • the algorithmic process of calculating the edit distance between character strings includes:
  • d[i,j] steps (you can use a two-dimensional array to save this value), which represents the minimum number of steps required to convert the string s[1...i] to the string t[1...j] .
  • the first row of the initialization matrix is 0 to n, and the first column is 0 to m.
  • Matrix[0][j] represents the value in row 1 and column j-1. This value represents the number of operations required to convert the string s[1...0] to t[1..j]. Obviously An empty string is converted to a string of length j, only j times of add operations are required, so the value of matrix[0][j] should be j, and other values can be deduced by analogy.
  • the magnitude between the calculated first correlation and the preset first threshold it is determined whether to perform a single round of dialogue or multiple rounds of dialogue to identify the user's intention. If the first correlation is greater than the preset first threshold, then multiple A round of dialogue recognizes the user's intention, and if the first correlation is less than a preset first threshold, a single round of dialogue is performed to recognize the user's intention.
  • the user's intention is identified according to the following information, and the result of user's intention identification is obtained, including:
  • Step 103 If the correlation between the first dialogue state and the second dialogue state is greater than or equal to a preset first threshold, obtain system feedback information corresponding to the first dialogue state;
  • the machine system that talks to the user in the first dialog state will automatically feed back a reply message.
  • the reply message is system feedback information
  • the system feedback information is implemented by the dialog management module.
  • the dialog management module will select the system feedback behavior that needs to be performed according to the first dialog state and the second dialog state, that is, system feedback information; if the system feedback information needs to interact with the user, then the language is generated
  • the module will be triggered to generate natural language or system speech; finally, the generated language is read aloud to the user by the speech synthesis module.
  • the main tasks of dialogue management include: dialogue state maintenance and system decision making.
  • the dialogue state maintenance includes maintaining and updating the dialogue state.
  • the dialogue state at time t+1 is St+1 , which depends on the state St at the previous time t , and the system behavior at the previous time t , and the current time User behavior a t+1 corresponding to t+1. It can be written as S t+1 ⁇ S t +a t +a t+1 to generate system decisions.
  • the system feedback behavior is generated to decide what to do next.
  • the system feedback behavior represents the dialogue based on the user's input State, the feedback behavior made by the system.
  • the input information of the dialogue management model is the user's voice information and the current dialogue state obtained by analyzing the user's voice information, and its output is the next system feedback behavior and updated dialogue status. Therefore, the more semantic information carried in the input information, the more accurate the information fed back by the dialogue management module.
  • the corresponding dialogue status includes: film and television, actors are animals, comedy and family dramas are genres, and other information related to Peppa Pig.
  • the system feedback information corresponding to the dialogue state is: video search. If the following information is: the third episode of the first season, through speech recognition and semantic analysis of the following information, the second dialogue state corresponding to the following information is: TV series or cartoons, episode 3, multi-season plot, etc., By calculating the similarity between the first dialogue state and the second dialogue state, it can be obtained that the correlation between the first dialogue state and the second dialogue state is greater than the preset first threshold, and it is necessary to obtain information about the first dialogue state.
  • System feedback Search for the third episode of the first season of Peppa Pig.
  • the above-mentioned dialogue management module controls the process of man-machine dialogue, and determines the reaction to the user at the moment based on the dialogue history information.
  • the most common multi-round dialogue is task-driven.
  • the user has a clear purpose such as ordering food and ticketing. User needs are more complex and have many restrictions. Therefore, consultation responses with relatively complex content need to be presented in multiple rounds.
  • users can continuously modify or improve their own needs.
  • the machine can also help users find satisfactory results by asking, clarifying, or confirming.
  • the dialogue process is as shown in Figure 4. The user and the system realize information communication through question and answer.
  • the user sends out a voice message: Hi, I want to order a meal and realize the transmission of voice commands.
  • voice command The system can be a voice robot, or other devices that can recognize the user's voice information
  • analyze the voice command and identify the key information contained in the voice command: restaurant, then the system will feed back the inquiry information: what type do you like What about the food, and the feedback dialogue behavior: food
  • the user sends out the voice message again: I like to eat Gongbao chicken
  • the system receives the keyword contained in the voice message: Gongbao chicken, According to the received information, the user feedback confirmation, and finally a satisfactory ordering effect is obtained.
  • Step S104 If the correlation between the system feedback information and the second dialogue state is less than a preset second threshold, the user's intention is identified according to the following information, and the user's intention identification result is obtained.
  • the correlation between the system feedback information corresponding to the first dialogue state and the second dialogue state acquired in the above step S103 is less than the preset second threshold, it is determined that the previous round of dialogue information and the next round of dialogue The information is low in relevance. Only the following information is used to identify the user's intention. Otherwise, it is determined that the previous round of dialogue information is highly relevant to the next round of dialogue information, and the following information and the relevant content of the above information are combined to determine the user's intention. Recognition.
  • step S104 if the correlation between the system feedback information and the second dialogue state is less than the preset second threshold, the user intention is identified according to the following information, and the user intention identification result is obtained before the step ,Also includes:
  • the second correlation between the system feedback information and the second dialogue state is calculated according to the obtained slot information.
  • the step of calculating the second correlation according to the third slot information and the second slot information includes:
  • the second correlation is calculated according to the size of the edit distance.
  • the calculation principle is the same as the calculation principle of the correlation between the first slot information and the second slot information in the above steps.
  • the method before the step of calculating the first correlation between the first dialogue state and the second dialogue state, the method further includes:
  • the first dialogue state and the second dialogue state contain It is judged whether the slot is filled completely. If it is not filled completely, it will be filled completely, and then the correlation between the two will be calculated.
  • the method further includes:
  • the slot corresponding to the system feedback information and/or the second dialogue state is not filled completely, the calculation accuracy of the correlation may be low. Therefore, in the above steps, the slots contained in the system feedback information and the second dialogue state are not fully filled. It is judged whether the filling is complete. If it is not filled, it will be filled completely, and then the correlation between the two will be calculated.
  • the step of identifying the user's intention by combining the first dialogue state, the system feedback information and the following information, and obtaining the user's intention recognition result includes:
  • the first dialogue state and/or the correlation between the system feedback information and the second dialogue state meets the preset threshold condition
  • the first dialogue state and the system feedback information contained in the first dialogue state will be told to you Character information, the user’s intention is recognized, and the search results for the above information are obtained.
  • the search results of the above information the following information is searched, so as to feed back the above information and the following information sent by the user. Corresponding user instructions, and search results.
  • a single round of dialogue is used to identify the user's intention, that is, only the following
  • the information is combined to identify the user's intention, and the result of the user's intention identification is obtained.
  • the step of recognizing the user's intention according to the following information and obtaining the result of the user's intention recognition includes:
  • the dialogue system in order to prevent the user from sending out the same voice information before and after, the dialogue system repeatedly calculates the correlation of the same voice, which leads to an increase in the amount of system information processing tasks.
  • the dialogue system Before the steps of the first dialogue state of the text information and the system feedback information corresponding to the first dialogue state, and the second dialogue state of the following information, it further includes:
  • Step H1 first determine whether the above information is the same as the following information, if they are the same, perform step H2, otherwise, perform step H3;
  • Step H2 reacquire the following information.
  • Step H3 acquiring the first dialogue state of the above information and the second dialogue state of the following information
  • Step H4 Calculate the first correlation between the first dialogue state and the second dialogue state; determine whether the first correlation is greater than a preset first threshold; if it is less, go to step H5, otherwise go to step H6;
  • Step H5 Obtain the system feedback information corresponding to the first dialogue state, and calculate the second correlation between the system feedback information and the second dialogue state, and the second dialogue state and the system feedback Whether the second correlation between the information is lower than the preset second threshold, if yes, execute step H7, if not, execute step H8;
  • Step H6 It is determined that there is a correlation between the two rounds of dialogue, then enter the opposite round dialogue, obtain the system feedback information corresponding to the first dialogue state, and combine the first dialogue state, the system feedback information, and the second dialogue state. Recognition of user intent.
  • Step H7 This conversation can be used as a single-round conversation to identify the user's intention.
  • Step H8 This dialogue performs multiple rounds of dialogue, and the user's intention needs to be identified in combination with the first dialogue state, system feedback information, and second dialogue state.
  • the corresponding dialogue status includes: film and television, actors are animals, comedies and family dramas are genres, and other information related to Peppa Pig.
  • the system feedback information corresponding to the dialogue state is: video search. If the following information is: what's the weather today, it can be obtained by speech recognition and semantic analysis of the following information, and the second dialogue state corresponding to the following information is: geographic location, today, temperature, rain, etc., by calculating the first dialogue
  • the similarity between the state and the second dialogue state it can be obtained that the correlation between the first dialogue state and the second dialogue state is lower than the preset first threshold, then it is judged that the following information is not related to the above information . Therefore, it is not possible to make system feedback information for the following information based on the results of the above information. It is necessary to search for the second dialogue state again and make system feedback information for the second dialogue state: search for the current user’s location today weather.
  • the corresponding dialogue state content includes: movies, genres are comedy and family drama, and the system feedback information corresponding to the dialogue state is: movie search.
  • the following information is: Peppa Pig
  • the corresponding dialogue state content includes: film and television
  • the actor is an animal
  • the genre is comedy and family drama
  • other information related to Peppa Pig which corresponds to the dialogue state
  • the system feedback information is: comedy movie search.
  • this embodiment also discloses a computer device, as shown in FIG. 5, including a memory and a processor, the memory storing a computer program, wherein when the processor executes the computer program Implement the steps of the method.
  • this embodiment also discloses a computer-readable storage medium on which a computer program is stored, wherein the steps of the method are implemented when the computer program is executed by a processor.
  • a computer device can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field Programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to perform the above methods.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field Programmable gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to perform the above methods.
  • the correlation between the dialogue states corresponding to the voice information of the front and rear dialogues is calculated to determine whether the correlation exceeds a certain threshold If it is not exceeded, the user’s intention will be recognized only for the next round of dialogue state. If it is exceeded, it will be judged whether the correlation between the system behavior of the previous round of dialogue and the dialogue state of the next round of dialogue exceeds a certain threshold. If it is not exceeded, the user's intention is identified based on the dialogue state of the next round alone; if it is exceeded, the user's intention is identified by combining the dialogue state of the previous round with the second dialogue state.
  • this embodiment fully considers the relevance of the information between the front and back rounds of dialogue, when there is a big difference in the information between the two, the latter round of dialogue will be used as a separate piece of information for user intent analysis, so that a more accurate analysis can be obtained. As a result, it provides a basis for realizing accurate feedback on information sent by users.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Human Computer Interaction (AREA)
  • Machine Translation (AREA)

Abstract

La présente invention concerne un procédé d'identification d'intention d'utilisateur dans un dialogue à plusieurs cycles, et un dispositif associé. Ledit procédé comprend les étapes suivantes : acquérir un premier état de dialogue lié à des informations précédentes et un second état de dialogue lié à des informations suivantes, dans un cycle précédent et un cycle suivant de dialogues d'un dialogue à plusieurs cycles ; et calculer une première corrélation entre le premier état de dialogue et le second état de dialogue, et déterminer, selon la grandeur de la première corrélation, l'opportunité de mettre en oeuvre une identification d'intention d'utilisateur d'un dialogue à un seul cycle. Comme la corrélation des informations entre les cycles précédent et suivant de dialogues est pleinement prise en compte dans le présent mode de réalisation, si lesdites informations sont considérablement différentes, une analyse d'intention d'utilisateur est mise en oeuvre au moyen du cycle suivant de dialogue en tant qu'élément d'informations indépendant, ce qui permet d'obtenir un résultat d'analyse plus précis et de fournir ainsi une base pour mettre en oeuvre une rétroaction précise sur des informations envoyées par un utilisateur.
PCT/CN2020/103922 2019-09-04 2020-07-24 Procédé d'identification d'intention d'utilisateur dans un dialogue à plusieurs cycles, et dispositif associé WO2021042902A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910833404.1A CN112445902A (zh) 2019-09-04 2019-09-04 一种多轮对话中用户意图的识别方法及相关设备
CN201910833404.1 2019-09-04

Publications (1)

Publication Number Publication Date
WO2021042902A1 true WO2021042902A1 (fr) 2021-03-11

Family

ID=74734481

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/103922 WO2021042902A1 (fr) 2019-09-04 2020-07-24 Procédé d'identification d'intention d'utilisateur dans un dialogue à plusieurs cycles, et dispositif associé

Country Status (2)

Country Link
CN (1) CN112445902A (fr)
WO (1) WO2021042902A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113076408A (zh) * 2021-03-19 2021-07-06 联想(北京)有限公司 一种会话信息的处理方法及装置
CN113806503A (zh) * 2021-08-25 2021-12-17 北京库睿科技有限公司 一种对话融合方法和装置及设备

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8868548B2 (en) * 2010-07-22 2014-10-21 Google Inc. Determining user intent from query patterns
CN105704013A (zh) * 2016-03-18 2016-06-22 北京光年无限科技有限公司 基于上下文的话题更新数据处理方法及装置
CN106649739A (zh) * 2016-12-23 2017-05-10 深圳市空谷幽兰人工智能科技有限公司 多轮交互信息继承识别方法、装置以及交互系统
CN107133345A (zh) * 2017-05-22 2017-09-05 北京百度网讯科技有限公司 基于人工智能的交互方法和装置
CN107193978A (zh) * 2017-05-26 2017-09-22 武汉泰迪智慧科技有限公司 一种基于深度学习的多轮自动聊天对话方法及系统
CN110136705A (zh) * 2019-04-10 2019-08-16 华为技术有限公司 一种人机交互的方法和电子设备

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10726831B2 (en) * 2014-05-20 2020-07-28 Amazon Technologies, Inc. Context interpretation in natural language processing using previous dialog acts
CN107369443B (zh) * 2017-06-29 2020-09-25 北京百度网讯科技有限公司 基于人工智能的对话管理方法及装置
CN109446306A (zh) * 2018-10-16 2019-03-08 浪潮软件股份有限公司 一种基于任务驱动的多轮对话的智能问答方法
CN109616108B (zh) * 2018-11-29 2022-05-31 出门问问创新科技有限公司 多轮对话交互处理方法、装置、电子设备及存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8868548B2 (en) * 2010-07-22 2014-10-21 Google Inc. Determining user intent from query patterns
CN105704013A (zh) * 2016-03-18 2016-06-22 北京光年无限科技有限公司 基于上下文的话题更新数据处理方法及装置
CN106649739A (zh) * 2016-12-23 2017-05-10 深圳市空谷幽兰人工智能科技有限公司 多轮交互信息继承识别方法、装置以及交互系统
CN107133345A (zh) * 2017-05-22 2017-09-05 北京百度网讯科技有限公司 基于人工智能的交互方法和装置
CN107193978A (zh) * 2017-05-26 2017-09-22 武汉泰迪智慧科技有限公司 一种基于深度学习的多轮自动聊天对话方法及系统
CN110136705A (zh) * 2019-04-10 2019-08-16 华为技术有限公司 一种人机交互的方法和电子设备

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WU, JIALI: "Technology Researches of Query Refinement Based on User Intent", CHINESE MASTER'S THESES FULL-TEXT DATABASE, 1 December 2015 (2015-12-01), pages 1 - 64, XP055790034, ISSN: 1674-0246 *

Also Published As

Publication number Publication date
CN112445902A (zh) 2021-03-05

Similar Documents

Publication Publication Date Title
US11790006B2 (en) Natural language question answering systems
US11442932B2 (en) Mapping natural language to queries using a query grammar
US11347783B2 (en) Implementing a software action based on machine interpretation of a language input
US11394667B2 (en) Chatbot skills systems and methods
WO2020007224A1 (fr) Procédé et appareil de construction de graphe de connaissances et de réponse intelligente, dispositif et support de stockage
US8190627B2 (en) Machine assisted query formulation
US8260809B2 (en) Voice-based search processing
WO2021051866A1 (fr) Procédé et appareil de détermination de résultat de jugement d'affaire, dispositif et support de stockage lisible par ordinateur
US7822699B2 (en) Adaptive semantic reasoning engine
US10402490B1 (en) Edit distance based spellcheck
US20090287626A1 (en) Multi-modal query generation
US11762848B2 (en) Combining parameters of multiple search queries that share a line of inquiry
WO2018045646A1 (fr) Procédé et dispositif à base d'intelligence artificielle pour interaction humain-machine
US20130124194A1 (en) Systems and methods for manipulating data using natural language commands
US10930272B1 (en) Event-based semantic search and retrieval
WO2021042902A1 (fr) Procédé d'identification d'intention d'utilisateur dans un dialogue à plusieurs cycles, et dispositif associé
TW202334839A (zh) 用於問題回答過程的上下文澄清和消歧
US20090006344A1 (en) Mark-up ecosystem for searching
US20230409836A1 (en) Query construction from conversational inputs
US10515076B1 (en) Generating query answers from a user's history
CN108959327B (zh) 一种业务处理方法、装置及计算机可读存储介质
US11960514B1 (en) Interactive conversation assistance using semantic search and generative AI
US20240160844A1 (en) Synonym searching system and method
US20240054991A1 (en) Spoken query processing for image search
US20230161808A1 (en) Performing image search based on user input using neural networks

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20861031

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 20/07/2022)

122 Ep: pct application non-entry in european phase

Ref document number: 20861031

Country of ref document: EP

Kind code of ref document: A1