CN115905513B - Dialogue abstracting method based on denoising type question and answer - Google Patents
Dialogue abstracting method based on denoising type question and answer Download PDFInfo
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Abstract
The invention relates to the technical field of role-oriented dialogue abstracts and discloses a dialogue abstracting method based on denoising type question and answer; the customer service abstract is generated by using the modeling method based on the question and answer, so that the relation between the customer abstract and the customer service abstract is considered, and the information from the customer abstract is integrated, so that the generated customer service abstract is more matched with the customer abstract, and the quality of the customer service abstract is improved; the invention uses the user abstract generated by the user abstract module to replace the user abstract used in the traditional method training model as a problem through a denoising mechanism, and the user abstract is spliced with the dialogue and then sent to a customer service abstract encoder; the data used by the model in the training process is more suitable for the actual use scene of the model, and the performance of generating customer service abstract by the model is improved.
Description
Technical Field
The invention relates to the technical field of role-oriented dialog abstracts, in particular to a dialog abstracting method based on denoising type question and answer.
Background
Role-oriented dialog extraction refers to the separate generation of abstracts for different roles in a dialog. The invention solves the following two technical problems:
existing abstracting methods tend to generate abstracts for different roles separately, regardless of their potential relationships, which results in abstracts of different roles not matching in some scenarios, e.g., user questions involved in user abstracts have no corresponding answers in customer service abstracts. In this regard, the present invention proposes to use a question and answer pattern based abstract generation method to generate corresponding answers to questions in each user abstract.
Existing question-answering models use manually annotated user questions as input during training. However, for role-oriented dialog abstract tasks, the trained model may use user problems generated by the model as input when in actual use. Because there is often a difference between the user problem generated by the model and the manually noted user problem, noise that may mislead the model exists in the manually noted user problem used during training. In this regard, the invention provides a question-answering architecture with a denoising mechanism, which effectively solves the problem of noise in manually marked user problems.
Disclosure of Invention
In order to solve the technical problems, the invention provides a dialogue abstracting method based on denoising type question and answer.
In order to solve the technical problems, the invention adopts the following technical scheme:
a dialogue abstract method based on denoising type question and answer inputs a given dialogue into a dialogue abstract model and outputs a customer service abstract; the dialogue abstract model comprises a user abstract module, a problem integration module, a customer service abstract encoder and a customer service abstract decoder;
the training method of the dialogue abstract model comprises the following steps:
step one: for a given dialog d=d 1 …d n Predictive generation of user summaries using a training-completed user summary module
The method comprises the steps of carrying out a first treatment on the surface of the Wherein d is 1 …d n Represents n sentences in dialog D, +.>
Representing user abstract +.>
N words of (a);
step two: applying a denoising mechanism in the problem integration module to abstract the user with manual annotation U=u 1 …u N User digest generated by replacing user digest module
I.e. user abstract +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein u is 1 …u N Representing N words in the manually marked user abstract U;
step three: in the problem integration module, the denoised user abstract is extracted
As a problem, with dialogue d=d 1 …d n Splicing to obtain spliced text Q=d 1 …d n [SEP]U, wherein [ SEP ]]Characters representing the boundary between the markup dialog and the question;
step four: sending the spliced text Q to a customer service abstract encoder to obtain a spliced text vector h of the spliced text Q output by the customer service abstract encoder q ；
Step five: manual marked customer service abstract a=a is recorded 1 …a M Wherein a is 1 …a M Representing M words in customer service abstract A; for the following
Handle h q The first j words A of the customer service abstract A marked manually j =a 1 …a j Sending the text to a customer service abstract decoder to obtain the j+1th word of the customer service abstract predicted by the dialogue abstract model>
The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining all words +.>
The method comprises the steps of carrying out a first treatment on the surface of the When j=0, a j =[CLS]，/>
Wherein [ CLS ]]Is a character marking the beginning of the abstract;
step six: each word of customer service abstract predicted by dialogue abstract model
Each word a of customer service abstract marked by manual j+1 By contrast, by cross entropy loss function L 2 Calculating Loss of Loss 2 ：
Step seven: through back propagation algorithm and Loss 2 And updating parameters in the customer service digest encoder and the customer service digest decoder.
Further, the user digest module comprises a user digest encoder and a user digest decoder; in the first step, the training process of the user abstract module is as follows:
inputting the conversation D into a user digest encoder to obtain a user digest conversation vector h of the conversation D output by the user digest encoder d ；
For the following
User abstract dialogue vector h d Manually noted user digest U's first i words U i =u 1 …u i Sending the word I+1 to a user digest decoder to obtain the user digest (i+1) predicted by the user digest module>
Thereby obtaining all words +.>
The method comprises the steps of carrying out a first treatment on the surface of the When i=0, U i =[CLS]，
；
Predicting individual words by a user digest module
Each word u of user abstract with manual marking i+1 By contrast, by cross entropy loss function L 1 Calculating Loss of Loss 1 ：
Through back propagation algorithm and Loss 1 And updating parameters of the user abstract module.
Further, in the first step, the process of generating the user digest by using the user digest module which is completely trained is as follows:
taking the conversation D as the input of a user digest module, sending the conversation D into a user digest encoder to obtain a user digest conversation vector h of the conversation D output by the user digest encoder d ；
For a pair of
Handle h d And the first i words that the user digest module has predicted to be generated
Sending the user digest to a user digest decoder to obtain the (i+1) th word of the user digest predicted by the user digest module>
Thereby obtaining all words +.>
I.e. user abstract +.>
。
Further, the process of inputting a given conversation into the conversation summary model and outputting a customer service summary is as follows:
generating a given dialog d=d using a training-completed user digest module 1 …d n User digest of (a)
；
In the problem integration module, user abstract is carried out
As a problem, with dialogue d=d 1 …d n Splicing to obtain a new spliced text Q=d 1 …d n [SEP]/>
Wherein [ SEP ]]Is a character marking the boundary between a dialog and a question;
sending the spliced text Q to a customer service abstract encoder to obtain a spliced text vector h of the spliced text Q output by the customer service abstract encoder q ；
Handle h q First j words of customer service abstract which have been generated by dialogue abstract model
Sending the text to a customer service abstract decoder to obtain the j+1th word of the customer service abstract predicted by the dialogue abstract model>
Thereby obtaining all predicted words of the dialogue abstract model +.>
I.e. customer service abstract->
。
Notably, in role-oriented conversation abstract tasks, particularly user and customer service conversation abstract tasks, customer service abstract generation is difficult. The main focus and improvement of the invention is also the abstract of customer service dialogue content.
Compared with the prior art, the invention has the beneficial technical effects that:
the customer service abstract is generated by using the modeling method based on the question and answer, so that the relation between the customer abstract and the customer service abstract is considered, and the information from the customer abstract is integrated, so that the generated customer service abstract is more matched with the customer abstract, and the quality of the customer service abstract is improved.
The invention uses the user abstract generated by the user abstract module to replace the user abstract used in the traditional method training model as a problem through a denoising mechanism, and the user abstract is spliced with the dialogue and then sent to a customer service abstract encoder; the data used by the model in the training process is more suitable for the actual use scene of the model, and the performance of generating customer service abstract by the model is improved.
Drawings
Fig. 1 is an overall flow chart of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
The invention is applied to role-oriented dialogue abstract tasks, for example, in dialogue between a user and customer service, user abstract and customer service abstract are required to be generated respectively.
As shown in fig. 1, the dialogue digest model in the present invention includes a user digest module, a problem integration module, a customer service digest encoder, and a customer service digest decoder; the user digest module includes a user digest encoder and a user digest decoder. And inputting the given dialogue into a dialogue abstract model, and outputting the customer service abstract.
The training method of the dialogue abstract model comprises the following steps:
s1: for a given dialog d=d 1 …d n Predictive generation of user summaries using a training-completed user summary module
The method comprises the steps of carrying out a first treatment on the surface of the Wherein d is 1 …d n Represents n sentences in dialog D, +.>
Representing user abstract +.>
N words of (a);
s2: applying a denoising mechanism in the problem integration module to abstract the user with manual annotation U=u 1 …u N User digest generated by replacing user digest module
I.e. user abstract +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein u is 1 …u N Representing N words in the manually marked user abstract U;
s3: in the problem integration module, the denoised user abstract is extracted
As a problem, with dialogue d=d 1 …d n Splicing to obtain spliced text Q=d 1 …d n [SEP]U, wherein [ SEP ]]Characters representing the boundary between the markup dialog and the question;
s4: sending the spliced text Q to a customer service abstract encoder to obtain a spliced text vector h of the spliced text Q output by the customer service abstract encoder q ；
S5: manual marked customer service abstract a=a is recorded 1 …a M Wherein a is 1 …a M Representing M words in customer service abstract A; for the following
Handle h q The first j words A of the customer service abstract A marked manually j =a 1 …a j Sending the text to a customer service abstract decoder to obtain the j+1th word of the customer service abstract predicted by the dialogue abstract model>
The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining all words +.>
The method comprises the steps of carrying out a first treatment on the surface of the When j=0, a j =[CLS]，/>
Wherein [ CLS ]]Is a character marking the beginning of the abstract;
s6: each word of customer service abstract predicted by dialogue abstract model
Each word a of customer service abstract marked by manual j+1 By contrast, by cross entropy loss function L 2 Calculating Loss of Loss 2 ：
S7: through back propagation algorithm and Loss 2 And updating parameters in the customer service digest encoder and the customer service digest decoder.
In S1, the process of generating the user digest by using the user digest module for the completion training is as follows:
s11: will dialogue d=d 1 …d n As the input of the user digest module, the user digest is sent to the user digest encoder to obtain the user digest dialogue vector h of the dialogue D output by the user digest encoder d 。
S12: for a pair of
Handle h d The first i words that have been generated by the user digest module
Sending the user digest to a user digest decoder to obtain the (i+1) th word of the user digest predicted by the user digest module>
Thereby obtaining all words +.>
I.e. user abstract +.>
. In particular, when i=0, U i =[CLS]，/>
=[CLS]Wherein [ CLS ]]Is a character marking the beginning of the abstract.
The training process of the user abstract module is as follows:
inputting the dialogue D into a user digest encoder to obtain a user digest dialogue vector h of the dialogue D output by the encoder d ；
User abstract for recording manual annotation is U=u 1 …u N Wherein u is 1 …u N Representing N words in the user abstract U; for the following
User abstract dialogue vector h d Manually noted user digest U's first i words U i =u 1 …u i Sending the word I+1 to a user digest decoder to obtain the user digest (i+1) predicted by the user digest module>
Thereby obtaining all words +.>
；
Predicting individual words by a user digest module
Each word u of user abstract with manual marking i+1 By contrast, by cross entropy loss function L 1 Calculating Loss of Loss 1 ：
Through back propagation algorithm and Loss 1 And updating parameters of the user abstract module.
The process of generating a customer digest using the session digest model is as follows:
generating a given dialog d=d using a training-completed user digest module 1 …d n User digest of (a)
；
In the problem integration module, user abstract is carried out
As a problem, with dialogue d=d 1 …d n Splicing to obtain a new spliced text Q=d 1 …d n [SEP]/>
Wherein [ SEP ]]Is a character marking the boundary between a dialog and a question;
sending the spliced text Q to a customer service abstract encoder to obtain a spliced text vector h of the spliced text Q output by the customer service abstract encoder q ；
Handle h q First j words of customer service abstract which have been generated by dialogue abstract model
Sending the text to a customer service abstract decoder to obtain the j+1th word of the customer service abstract predicted by the dialogue abstract model>
Thereby obtaining all predicted words of the dialogue abstract model +.>
I.e. customer service abstract->
. In particular, when j=0, a j =[CLS]Wherein [ CLS ]]Is a character marking the beginning of the abstract.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a single embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to specific embodiments, and that the embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
Claims (4)
1. A dialogue abstracting method based on denoising type question and answer is characterized in that a given dialogue is input into a dialogue abstracting model, and customer service abstracts are output; the dialogue abstract model comprises a user abstract module, a problem integration module, a customer service abstract encoder and a customer service abstract decoder;
the training method of the dialogue abstract model comprises the following steps:
step one: for a given dialog d=d 1 …d n Predictive generation of user summaries using a training-completed user summary module
The method comprises the steps of carrying out a first treatment on the surface of the Wherein d is 1 …d n Represents n sentences in dialog D, +.>
Representing user abstract +.>
N words of (a);
step two: applying a denoising mechanism in the problem integration module to abstract the user with manual annotation U=u 1 …u N User digest generated by replacing user digest module
I.e. user abstract +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein u is 1 …u N Representing N words in the manually marked user abstract U;
step three: in the problem integration module, the denoised user abstract is extracted
As a problem, with dialogue d=d 1 …d n Splicing to obtain spliced text Q=d 1 …d n [SEP]U, wherein [ SEP ]]Characters representing the boundary between the markup dialog and the question;
step four: sending the spliced text Q to a customer service abstract encoder to obtain a spliced text vector h of the spliced text Q output by the customer service abstract encoder q ；
Step five: manual marked customer service abstract a=a is recorded 1 …a M Wherein a is 1 …a M Representing M words in customer service abstract A; for the following
Handle h q The first j words A of the customer service abstract A marked manually j =a 1 …a j Sending the text to a customer service abstract decoder to obtain the j+1th word of the customer service abstract predicted by the dialogue abstract model>
The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining all words +.>
The method comprises the steps of carrying out a first treatment on the surface of the When j=0, a j =[CLS]，/>
Wherein [ CLS ]]Is a character marking the beginning of the abstract;
step six: each word of customer service abstract predicted by dialogue abstract model
Each word a of customer service abstract marked by manual j+1 By contrast, by cross entropy loss function L 2 Calculating Loss of Loss 2 ：
Step seven: through back propagation algorithm and Loss 2 And updating parameters in the customer service digest encoder and the customer service digest decoder.
2. The method for abstracting a dialogue based on a denoising question and answer according to claim 1, wherein: the user digest module comprises a user digest encoder and a user digest decoder; in the first step, the training process of the user abstract module is as follows:
inputting the conversation D into a user digest encoder to obtain a user digest conversation vector h of the conversation D output by the user digest encoder d ；
For the following
User abstract dialogue vector h d Manually noted user digest U's first i words U i =u 1 …u i Sending the word I+1 to a user digest decoder to obtain the user digest (i+1) predicted by the user digest module>
Thereby obtaining all words +.>
The method comprises the steps of carrying out a first treatment on the surface of the When i=0, U i =[CLS]，
；
Predicting individual words by a user digest module
Each word u of user abstract with manual marking i+1 By contrast, by cross entropy loss function L 1 Calculating Loss of Loss 1 ：
Through back propagation algorithm and Loss 1 And updating parameters of the user abstract module.
3. The method for abstracting a dialogue based on a denoising question and answer according to claim 1, wherein: in the first step, the process of generating the user digest by using the user digest module for training is as follows:
taking the conversation D as the input of a user digest module, sending the conversation D into a user digest encoder to obtain a user digest conversation vector h of the conversation D output by the user digest encoder d ；
For a pair of
Handle h d And the first i words that the user digest module has predicted to be generated
Sending the user digest to a user digest decoder to obtain the (i+1) th word of the user digest predicted by the user digest module>
Thereby obtaining all words +.>
I.e. user abstract +.>
。
4. The method for abstracting a dialogue based on a denoising question and answer according to claim 1, wherein: the process of inputting a given conversation into the conversation summary model and outputting a customer service summary is as follows:
user summary module generation using complete trainingFixed dialog d=d 1 …d n User digest of (a)
；
In the problem integration module, user abstract is carried out
As a problem, with dialogue d=d 1 …d n Splicing to obtain a new spliced text Q=d 1 …d n [SEP]/>
Wherein [ SEP ]]Is a character marking the boundary between a dialog and a question;
sending the spliced text Q to a customer service abstract encoder to obtain a spliced text vector h of the spliced text Q output by the customer service abstract encoder q ；
Handle h q First j words of customer service abstract which have been generated by dialogue abstract model
Sending the text to a customer service abstract decoder to obtain the j+1th word of the customer service abstract predicted by the dialogue abstract model>
Thereby obtaining all predicted words of the dialogue abstract model +.>
I.e. customer service abstract->
。
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