CN109885664A - A kind of Intelligent dialogue method, robot conversational system, server and storage medium - Google Patents
A kind of Intelligent dialogue method, robot conversational system, server and storage medium Download PDFInfo
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Abstract
The invention discloses a kind of Intelligent dialogue method, robot conversational system, server and storage mediums, which comprises the artificial customer service dialogue data for obtaining history accumulation extracts the QA question and answer in the dialogue data to deposit QA database；Classification and Entity recognition are carried out to all problems in QA database respectively, and class label and entity vector are increased separately to each problem；According to the classification and entity of problem to be replied, the QA question and answer comprising the same category and entity are filtered out to as candidate data set from QA database；The problem of problem to be replied is matched with the problem in candidate data set, is found with problem similarity highest to be replied, and the corresponding answer of using problem similarity highest to be replied the problem of is as the answer of problem to be replied as exporting.The present invention screens problem domain using textual classification model and NER, enhances the accuracy of FAQ reply, it is ensured that the features such as " capable of asking what what answers " rather than give an irrelevant answer.
Description
Technical field
The present invention relates to robots to talk with field, and in particular to a kind of Intelligent dialogue method, robot conversational system, service
Device and storage medium.
Background technique
The artificial customer service cost for training a high quality is very high, and there are it is online slowly, the working time is limited equal to be lacked
Point.The artificial above-mentioned disadvantage of customer service leads to artificial customer service recruitment, training, using difficulty, causes potential customers to be lost indirectly, gives enterprise
Cause huge loss.Therefore, the robot conversational system of a set of high quality is customized just at the urgent need of many enterprises.?
Robot conversational system urgent need in modern times doctor U.S.A field, that there is also this is huge for the robot conversational system in doctor U.S. field at present
Vacancy.
Since major part Yi Mei enterprise is all also docked using artificial customer service with client at this stage, all add up a large amount of
Artificial customer service dialogue data.By taking the beautifying medical hospital of Beijing as an example, adds up artificial customer service dialogue data and reach 2,100,000 left sides
It is right.These data cover a large amount of valuable dialogues of the enterprise in each project.
Summary of the invention
It is an object of the invention to a kind of Intelligent dialogue method, machine, for data basis, are proposed with enterprise's artificial customer service dialogue
Device people conversational system and storage medium can be quickly applied to medical and beauty treatment fields robot dialogue field.
The present invention provides a kind of Intelligent dialogue methods, include the following:
The artificial customer service dialogue data for obtaining history accumulation extracts the QA question and answer in the dialogue data to deposit QA data
Library；
Customer issue is formulated to data to data, and according to the QA question and answer of extraction from the QA question and answer of QA database extraction section
Several class scopes and several entity class ranges, and textual classification model and life is respectively trained to for training data with QA question and answer
Name entity recognition model；
With trained textual classification model and Named Entity Extraction Model respectively to all problems in QA database into
Row classification and Entity recognition, and class label and entity vector are increased separately to each problem, the class label of described problem is
Output of the problem Jing Guo trained textual classification model, the entity vector of described problem are that problem is real by trained name
The entity sets that body identification model is extracted are constituted；
Obtain problem to be replied from existing customer dialogue, and will be to be replied the problem of respectively by trained text point
Class model and Named Entity Extraction Model respectively obtain the generic and entity of problem to be replied；
According to the classification and entity of problem to be replied, the QA question and answer comprising the same category and entity are filtered out from QA database
To as candidate data set；
Problem to be replied is matched with the problem in candidate data set, is found highest with problem similarity to be replied
Problem, and the corresponding answer of using problem similarity highest to be replied the problem of is as the answer of problem to be replied as output.
Further, the entity vector of described problem is the vector of a regular length, the dimension and entity class of vector
Sum is equal, and element value in each dimension is 1 or 0, represents and includes or do not include the entity.
Further, in embodiments of the present invention, which comprises extracting the QA question and answer in dialogue data to preceding,
The data of the artificial customer service dialogue data of history accumulation are cleaned or screened.The mode of the data cleansing or screening is logical
The mode for crossing keyword search, regularity matching and human assistance screening, rejects meaningless dialogue.It is described meaningless
Dialogue comprising it is following any one: give an irrelevant answer, length is too short, unmanned response.
Further, it is preferred that the textual classification model is xgboost textual classification model.
Further, it is preferred that the deterministic process of the similarity is to convert sentence for problem using word2vec model
Vector calculates the Euclidean distance of sentence vector as index of similarity using the cosine law.
Further, it is preferred that the Entity recognition includes entity Boundary Recognition and determining entity class.
Correspondingly, the embodiment of the present invention, additionally provides a kind of robot conversational system, comprising: receiving unit, output are single
Member, at least one processor and the memory being connect at least one described processor communication；Wherein, the receiving unit
For receiving the conversation content of existing customer Yu the robot conversational system；The output unit is for exporting existing customer pair
Talk about the answer of problem in content；The memory is stored with the instruction that can be executed by least one described processor, described instruction
It is executed by least one described processor, so that at least one described processor executes above-mentioned Intelligent dialogue method.
Correspondingly, the present invention also provides a kind of servers, comprising: at least one processor；And with described at least one
The memory of a processor communication connection；Wherein, the memory is stored with the finger that can be executed by least one described processor
It enables, described instruction is executed by least one described processor, so that at least one described processor executes above-mentioned Intelligent dialogue side
Method.
Correspondingly, the computer readable storage medium is deposited the present invention also provides a kind of computer readable storage medium
Computer program is contained, the computer program realizes above-mentioned Intelligent dialogue method and step when being executed by processor.
Intelligent dialogue method, Intelligent dialogue system, server and storage medium provided by the invention, with existing artificial customer service
Compare, the present invention this using text classifier (Text-Classifier), name Entity recognition (Named Entity
Recognition, NER) and frequently asked questions similarity mode (FAQ Similarity) technology progress robot conversational system structure
It builds, the present invention is based on the FAQ conversational systems of artificial customer service data building, are a kind of conversational systems of high-efficient simple, can satisfy
The dialogue demand on basis, the method for the present invention screen problem domain using textual classification model and NER, enhance FAQ and return
Multiple accuracy, it is ensured that " can ask what what answers " rather than give an irrelevant answer.
Detailed description of the invention
Attached drawing described herein is used to provide to further understand invention, constitutes a part of the invention, the present invention
Illustrative embodiments and their description be used to explain the present invention, do not constitute improper limitations of the present invention, it should be apparent that, under
Attached drawing in the description of face is some embodiments of the present invention, for those of ordinary skill in the art, is not paying creativeness
Under the premise of labour, it is also possible to obtain other drawings based on these drawings.In the accompanying drawings:
Fig. 1 is a kind of schematic illustration of Intelligent dialogue method of the embodiment of the present invention 1；
Fig. 2 is a kind of process step schematic diagram of Intelligent dialogue method of the embodiment of the present invention 1.
Specific embodiment
In order to be clearer and more clear technical problems, technical solutions and advantages to be solved, tie below
Drawings and examples are closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.Based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
Embodiment 1
The present invention provides a kind of Intelligent dialogue methods, and specific implementation principle is as shown in Fig. 1, and the method includes as follows
Step, as shown in Fig. 2,
Step S1 obtains the artificial customer service dialogue data of history accumulation, extracts QA question and answer in the dialogue data to depositing
Enter QA database, the QA question and answer are to for problem and the corresponding answer of problem.
Step S2 formulates data to data, and according to the QA question and answer of extraction from the QA question and answer of QA database extraction section
Several class scopes of customer issue and several entity class ranges, and text classification is respectively trained to for training data with QA question and answer
Model and Named Entity Extraction Model；Preferably, in embodiments of the present invention, the QA question and answer from QA database extraction section
It is to extract 10% QA question and answer pair of the QA question and answer to total amount of QA database to data.
Step S3, with trained textual classification model and Named Entity Extraction Model respectively to all in QA database
Problem carries out classification and Entity recognition, and increases separately class label and entity vector, the classification of described problem to each problem
Label is output of the problem Jing Guo trained textual classification model, and the entity vector of described problem is problem by trained
The entity sets that Named Entity Extraction Model is extracted are constituted；The entity vector of described problem be regular length to
Amount, the dimension of vector is equal with entity class sum, and the element value in each dimension is 1 or 0, represents and includes or do not include this
Entity.As shown in table 1-2, example sentence " cut double-edged eyelid and want thousands of? " in, it can be extracted using Named Entity Extraction Model
Entity is " position organ " (double-edged eyelid) and " price " (thousands of), then is 1 in " position organ " and the element value of " price ".This hair
Bright embodiment carries out classification and Entity recognition to all problems in QA database, obtains entity as shown in Table 1, classification, asks
Corresponding table is inscribed, entity vector is as shown in table 2.
1 entity of table, classification, problem correspond to table
2 entity vector of table
| Price | Time | It is preferential | Position organ | Symptom | Material instrument | Expert | ···· |
| 1 | 0 | 0 | 1 | 0 | 0 | 0 | ···· |
Step S4 obtains problem to be replied from existing customer dialogue, and will be to be replied the problem of respectively by training
Textual classification model and Named Entity Extraction Model respectively obtain the generic and entity of problem to be replied；The present invention is implemented
Obtain that problem to be replied is " it is thousands of to cut double-edged eyelid? " in example, such as existing customer dialogue, it is " to cut double-edged eyelid by problem to be replied
It is thousands of? ", obtaining generic by trained textual classification model is " consulting price ", utilizes Named Entity Extraction Model
The entity that can be extracted is " position organ " (double-edged eyelid) and " price " (thousands of).
Step S5 is filtered out from QA database comprising the same category and entity according to the classification and entity of problem to be replied
QA question and answer to as candidate data set, in the embodiment of the present invention, problem to be replied is " it is thousands of to cut double-edged eyelid? " the problem of finding out
It is all the price that this project of double-edged eyelid is cut in inquiry；
Problem to be replied is matched with the problem in candidate data set, is found similar to problem to be replied by step S6
The problem of spending highest, and the corresponding answer of using problem similarity highest to be replied the problem of as the answer of problem to be replied as
Output.
Further, in order to more export more accurately answer, this hair states method and includes:
The QA question and answer in dialogue data are being extracted to preceding, the data of the artificial customer service dialogue data of history accumulation are being carried out clear
It washes or screens.The mode of data cleansing and screening is screened by keyword search, regularity matching and human assistance
Mode, the meaningless dialogues such as rejecting is given an irrelevant answer, length is too short and nobody responds, retains the dialogue data of high quality.
In order to train the textual classification model of high quality, in embodiments of the present invention, it is preferred that the textual classification model
For xgboost textual classification model.Xgboost (eXtreme Gradient Boosting) is by Chen Tianqi in gbdt
Improved integrated learning approach on the basis of (Gradient Boosting) can be carried out using the multithreading of CPU parallel automatically
It calculates, while algorithmically being improved and improving precision.Xgboost is an additivity regression model, is changed by boosting
Generation one group of weak learner of construction is put to the vote, to export optimal result.Relative to LR classifier (Logistic
Regression Classifier), xgboost classifier has the advantage that: not needing to do the normalization of feature, automatic progress
Feature selecting, model interpretation are preferable, are adapted to a variety of loss functions such as SquareLoss, LogLoss etc., utilize
Xgboost model can train the text classifier of high quality.
FAQ is the abbreviation of English Frequently Asked Questions, and the Chinese meaning is exactly that " that often asks asks
Topic ", or more generically it is called " frequently asked questions and corresponding answer ".FAQ is that the main means of online help are provided in current network, is passed through
Better possible often ask is organized to answer questions in advance, publication provides counseling services on webpage for user.It is proposed according to user
Problem, most close problem in matching database, and the output answered using the answer for being matched to problem as customer issue.It is common
Similarity calculating method be convert a vector for problem using word2vec model, utilize the cosine law to calculate sentence vector
Euclidean distance is as index of similarity.In practical applications, the answer for utilizing FAQ matching problem merely, is also frequently present answer
It is not in place, situations such as answering wrong field or give an irrelevant answer.
In the methods of the invention, it is preferred that the deterministic process of the similarity is to be turned problem using word2vec model
A vector is turned to, calculates the Euclidean distance of sentence vector as index of similarity using the cosine law.It should be noted that wait reply
The similarity of the problems in problem and candidate data set compares, and is not limited to institute's example way of the present invention, can also have other
Implementation method.
Further, in the method for the present invention, the Entity recognition includes entity Boundary Recognition and determining entity class.Name
Entity recognition (Named Entity Recognition, abbreviation NER), also referred to as " proper name identification " refer to and have in identification text
There is the entity of certain sense, mainly includes name, place name, mechanism name, proper noun etc..Name Entity recognition be information extraction,
Question answering system, syntactic analysis, machine translation, towards Semantic Web metadata mark etc. application fields important foundation work
Tool occupies an important position during natural language processing technique moves towards practical.Name Entity recognition generally includes two
Point: entity Boundary Recognition and determining entity class.
Embodiment 2
A kind of robot conversational system is provided in the embodiment of the present invention, the robot conversational system includes: to receive list
Member, output unit, at least one processor and the memory being connect at least one described processor communication；
Wherein, the receiving unit is used to receive the conversation content of existing customer Yu the robot conversational system；
The output unit is used to export the answer of problem in existing customer conversation content；
The memory is stored with the instruction that can be executed by least one described processor, and described instruction is by described at least one
A processor executes, so that at least one described processor executes Intelligent dialogue method and step.The embodiment of the present invention is intelligently right
It is identical as embodiment 1 to talk about method and step, repeats no more.
Embodiment 3
A kind of server is provided in the embodiment of the present invention, the server includes at least one processor；And with institute
State the memory of at least one processor communication connection；Wherein, be stored with can be by least one described processor for the memory
The instruction of execution, described instruction are executed by least one described processor, so that at least one described processor execution intelligence is right
Talk about method and step.The memory also stores QA database, the embodiment of the present invention Intelligent dialogue method and step and embodiment 1
It is identical, it repeats no more.
Embodiment 4
A kind of computer readable storage medium, the computer-readable recording medium storage are provided in the embodiment of the present invention
There is computer program, Intelligent dialogue method and step is realized when the computer program is executed by processor.It is described computer-readable
Storage medium also stores QA database, and the embodiment of the present invention Intelligent dialogue method and step is identical as embodiment 1, no longer superfluous
It states.
If realized in the form of software function module the present invention provides a kind of Intelligent dialogue method and as independent
Product when selling or using, also can store in a computer readable storage medium.Based on this understanding, of the invention
Substantially the part that contributes to existing technology can embody the technical solution of embodiment in the form of software products in other words
Out, which is stored in a storage medium, including some instructions are used so that server (can be a
People's computer, Cloud Server, the network equipment or be equipment comprising processor etc.) execute described in each embodiment of the present invention
The all or part of method.The computer readable storage medium includes but is not limited to read-only memory (ROM, Read Only
Memory), random access memory (RAM, Random Access Memory), USB flash disk, mobile hard disk, magnetic or disk etc.
The various media that can store program code.The embodiment of the present invention is not limited to any specific hardware and software and combines.
The above is only a preferred embodiment of the present invention, is merely illustrative of the technical solution of the present invention, it is to be understood that this hair
Bright to be not limited to above-described embodiment, enlightenment through the invention, those skilled in the art are in conjunction with known or the prior art, knowledge
The modification carried out, or equivalent substitution of some or all of the technical features also should be regarded as in protection of the invention
In range.
Claims (10)
1. a kind of Intelligent dialogue method, which is characterized in that the described method includes:
The artificial customer service dialogue data for obtaining history accumulation extracts the QA question and answer in the dialogue data to deposit QA database；
From the QA question and answer of QA database extraction section to data, and it is several according to the QA question and answer of extraction to formulate customer issue to data
Class scope and several entity class ranges, and textual classification model and name is respectively trained in fact to for training data with QA question and answer
Body identification model；
The all problems in QA database are divided respectively with trained textual classification model and Named Entity Extraction Model
Class and Entity recognition, and class label and entity vector are increased separately to each problem, the class label of described problem is problem
By the output of trained textual classification model, the entity vector of described problem is that problem is known by trained name entity
The entity sets that other model extracts are constituted；
Obtain problem to be replied from existing customer dialogue, and will be to be replied the problem of pass through trained text classification mould respectively
Type and Named Entity Extraction Model respectively obtain the generic and entity of problem to be replied；
According to the classification and entity of problem to be replied, the QA question and answer comprising the same category and entity are filtered out to work from QA database
For candidate data set；
Problem to be replied is matched with the problem in candidate data set, is found and problem similarity to be replied is highest asks
Topic, and the corresponding answer of using problem similarity highest to be replied the problem of is as the answer of problem to be replied as exporting.
2. Intelligent dialogue method according to claim 1, which is characterized in that
The entity vector of described problem is the vector of a regular length, and the dimension of vector is equal with entity class sum, each
Element value in dimension is 1 or 0, represents and includes or do not include the entity.
3. Intelligent dialogue method according to claim 1, which is characterized in that
The described method includes:
The QA question and answer in dialogue data are being extracted to preceding, to the data of the artificial customer service dialogue data of history accumulation carry out cleaning or
Screening.
4. Intelligent dialogue method according to claim 3, which is characterized in that
The mode of the data cleansing or screening is the side by keyword search, regularity matching and human assistance screening
Formula rejects meaningless dialogue.
5. Intelligent dialogue method according to claim 4, which is characterized in that
The meaningless dialogue comprising it is following any one: give an irrelevant answer, length is too short, unmanned response.
6. Intelligent dialogue method according to claim 1, which is characterized in that
The textual classification model is xgboost textual classification model.
7. Intelligent dialogue method according to claim 1, which is characterized in that
The deterministic process of the similarity is to convert a vector for problem using word2vec model, is calculated using the cosine law
The Euclidean distance of sentence vector is as index of similarity.
8. a kind of robot conversational system, which is characterized in that the robot conversational system include: receiving unit, output unit,
At least one processor and the memory being connect at least one described processor communication；
Wherein, the receiving unit is used to receive the conversation content of existing customer Yu the robot conversational system；
The output unit is used to export the answer of problem in existing customer conversation content；
The memory is stored with the instruction that can be executed by least one described processor, and described instruction is by described at least one
It manages device to execute, so that at least one described processor perform claim requires any one of 1 to the 7 Intelligent dialogue method.
9. a kind of server, which is characterized in that the server includes:
At least one processor；And the memory being connect at least one described processor communication；Wherein, the memory is deposited
The instruction that can be executed by least one described processor is contained, described instruction is executed by least one described processor, so that institute
It states at least one processor perform claim and requires any one of 1 to the 7 Intelligent dialogue method.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In,
The Intelligent dialogue method and step as described in any one of claim 1 to 7 is realized when the computer program is executed by processor.
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| CN110196931A (en) * | 2019-06-28 | 2019-09-03 | 北京蓦然认知科技有限公司 | A kind of dialogue generation method and device based on iamge description |
| CN110738511A (en) * | 2019-09-06 | 2020-01-31 | 苏宁金融科技(南京)有限公司 | Intelligent customer service method and device |
| CN110795559A (en) * | 2019-10-10 | 2020-02-14 | 上海易点时空网络有限公司 | Data processing method and device for customer service question answering |
| CN110969021A (en) * | 2019-12-23 | 2020-04-07 | 竹间智能科技（上海）有限公司 | Named entity recognition method, device, equipment and medium in single-round conversation |
| CN111062612A (en) * | 2019-12-17 | 2020-04-24 | 联想(北京)有限公司 | Construction method of auxiliary processing stream and electronic equipment |
| CN111340218A (en) * | 2020-02-24 | 2020-06-26 | 支付宝(杭州)信息技术有限公司 | Method and system for training problem recognition model |
| CN111737424A (en) * | 2020-02-21 | 2020-10-02 | 北京沃东天骏信息技术有限公司 | Question matching method, device, equipment and storage medium |
| CN112380875A (en) * | 2020-11-18 | 2021-02-19 | 杭州大搜车汽车服务有限公司 | Conversation label tracking method, device, electronic device and storage medium |
| WO2021051508A1 (en) * | 2019-09-18 | 2021-03-25 | 平安科技（深圳）有限公司 | Robot dialogue generating method and apparatus, readable storage medium, and robot |
| CN112613304A (en) * | 2020-12-17 | 2021-04-06 | 合肥讯飞数码科技有限公司 | Question answering method, electronic device and storage device |
| CN113010654A (en) * | 2021-03-17 | 2021-06-22 | 北京十一贝科技有限公司 | Question reply method and device applied to insurance industry, electronic equipment and medium |
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| CN110196931A (en) * | 2019-06-28 | 2019-09-03 | 北京蓦然认知科技有限公司 | A kind of dialogue generation method and device based on iamge description |
| CN110196931B (en) * | 2019-06-28 | 2021-10-08 | 北京蓦然认知科技有限公司 | Image description-based dialog generation method and device |
| CN110738511A (en) * | 2019-09-06 | 2020-01-31 | 苏宁金融科技(南京)有限公司 | Intelligent customer service method and device |
| WO2021051508A1 (en) * | 2019-09-18 | 2021-03-25 | 平安科技（深圳）有限公司 | Robot dialogue generating method and apparatus, readable storage medium, and robot |
| CN110795559A (en) * | 2019-10-10 | 2020-02-14 | 上海易点时空网络有限公司 | Data processing method and device for customer service question answering |
| CN111062612A (en) * | 2019-12-17 | 2020-04-24 | 联想(北京)有限公司 | Construction method of auxiliary processing stream and electronic equipment |
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| CN111340218A (en) * | 2020-02-24 | 2020-06-26 | 支付宝(杭州)信息技术有限公司 | Method and system for training problem recognition model |
| CN112380875A (en) * | 2020-11-18 | 2021-02-19 | 杭州大搜车汽车服务有限公司 | Conversation label tracking method, device, electronic device and storage medium |
| CN112613304A (en) * | 2020-12-17 | 2021-04-06 | 合肥讯飞数码科技有限公司 | Question answering method, electronic device and storage device |
| CN113010654A (en) * | 2021-03-17 | 2021-06-22 | 北京十一贝科技有限公司 | Question reply method and device applied to insurance industry, electronic equipment and medium |
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