# US 2021/0241147 A1 · Method and device for predicting pair of similar questions and electronic equipment

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0241147 A1
- Document ID: US20210241147A1
- Status: Published application
- Kind: A1
- Application number: 17/238,169
- Filed: 2021-04-22
- Published: 2021-08-05
- Earliest claimed: 2020-11-02
- Assignees: Beijing More Health Technology Group Co. Ltd.
- Applicants: Beijing More Health Technology Group Co. Ltd.
- Inventors: Dejie CHANG; Bangchang LIU; Shufeng GU; Hongwen ZHAO; Xiaobin LUO; Yikun ZHANG; Yunzhao WU; Chaozhen LIU; Hai WANG; Hangfei ZHANG; Ke JI
- Web page: https://patentgrep.com/patent/US20210241147A1

## CPC

- G06N 3/08: Learning methods
- G06F 18/2115: by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/22: Matching criteria, e.g. proximity measures
- G06F 18/24137: {Distances to cluster centroïds}
- G06N 3/045: Combinations of networks
- G06N 3/0499: Feedforward networks
- G06N 3/09: Supervised learning
- G06N 5/04: Inference or reasoning models
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

An embodiment of the present application provides a method and a device for predicting a pair of similar questions and an electronic equipment, wherein a pair of similar questions to be predicted are input into multiple different prediction models, and a prediction result output by each of the prediction models is obtained; a random disturbance parameter is added into an embedding layer of at least one of the prediction models; and voting operation is performed on multiple prediction results to obtain a final prediction result of the pair of similar questions to be predicted. According to the present application, the a random disturbance parameter is added into the embedding layer of the prediction model, so that over-fitting caused by over-learning of sample knowledge by the prediction model can be effectively prevented, and the prediction accuracy can be effectively improved by predicting the pair of similar questions utilizing the prediction model.

## Claims

### Claim 1

A method for predicting a pair of similar questions, comprising:
inputting a pair of similar questions to be predicted into multiple different prediction models to obtain a prediction result output by each of the prediction models;
adding a random disturbance parameter into an embedding layer of at least one of the prediction models; and
performing voting operation on multiple prediction results to obtain a final prediction result of the pair of similar questions to be predicted;
wherein each of the prediction models comprises a plurality of prediction sub-models, wherein each of the plurality of prediction sub-models is obtained by training the prediction model from a specific training sample set of the pair of similar questions and a training sample set of the pair of similar questions determined by an allocation function.

### Claim 2 (depends on 1)

The method according to claim 1, wherein inputting the pair of similar questions to be predicted into multiple different prediction models to obtain the prediction result output by each of the prediction models comprises:
inputting the pair of similar questions to be predicted into multiple prediction sub-models included in each of the prediction models to obtain a prediction sub-result output by each of the prediction sub-models; and
performing voting operation on multiple prediction sub-results to obtain the prediction results.

### Claim 3 (depends on 1)

The method according to claim 1, wherein the prediction sub-model is obtained by training the prediction model from the specific training sample set of the pair of similar questions and the training sample set of the pair of similar questions determined by the allocation function comprises:
obtaining an original training sample set of the pair of similar questions;
performing a training sample extension processing on the original training sample set of the pair of similar questions by utilizing a similarity transmission principle to obtain an extended training sample set of the pair of similar questions;
determining the training sample set of the pair of similar questions from the extended training sample set of the pair of similar questions based on the allocation function; and
training the prediction model by utilizing the training sample set of the pair of similar questions and the specific training sample set of the pair of similar questions to obtain the prediction sub-model.

### Claim 4 (depends on 3)

The method according to claim 3, wherein after obtaining the extended training sample set of the pair of similar questions, the method further comprises:
sequentially labeling each pair of training samples of the pair of similar questions in the extended training sample set of the pair of similar questions.

### Claim 5 (depends on 3)

The method according to claim 3, wherein determining the training sample set of the pair of similar questions from the extended training sample set of the pair of similar questions based on the allocation function comprises:
determining a first label from the extended training sample set of the pair of similar questions by utilizing a first function of the allocation function;
determining a second label from the extended training sample set of the pair of similar questions based on the first label by utilizing a second function of the allocation function; and
selecting an extended training sample set of the pair of similar questions between the first label and the second label as the training sample set of the pair of similar questions.

### Claim 6 (depends on 5)

The method according to claim 5, wherein the first function is:
i=AllNumber*radom(0,1)+offset;
wherein i represents the first label, i<AllNumber, AllNumber indicates a length of the extended training sample set of the pair of similar questions, offset represents an offset, offset <AllNumber,
and the offset is a positive integer.

### Claim 7 (depends on 6)

The method according to claim 6, wherein the second function is:
j=i+A%*AllNumber;
wherein j represents the second label, i≤j≤AllNumber, A is a positive integer, and 0≤A≤100.

### Claim 8 (depends on 1)

The method according to claim 1, wherein the similarity between each pair of specific training samples of the pair of similar questions in the specific training sample set of the pair of similar questions and the training sample set of the pair of similar questions is greater than a preset similarity; and
the step of obtaining the prediction sub-model by training the prediction model by utilizing the training sample set of the pair of similar questions and the specific training sample set of the pair of similar questions comprises:
training a first preset network layer number parameter of the prediction model based on the training sample set of the pair of similar questions, and obtaining a prediction preliminary model of the prediction model when a loss function of the prediction model converges; and
training a second preset network layer number parameter of the prediction preliminary model based on the specific training sample set of the pair of similar questions, and obtaining the prediction sub-model when the loss function of the prediction preliminary model converges.

### Claim 9 (depends on 1)

The method according to claim 1, wherein the random disturbance parameter is generated utilizing the following formula:
delta
=
1
1
+
exp
⁡
(
-
a
)
;
wherein delta represents the random disturbance parameter and a represents a parameter factor, −5≤a≤5.

### Claim 10

A device for predicting a pair of similar questions comprising:
an input module configured to input a pair of similar questions to be predicted into multiple different prediction models to obtain a prediction result output by each of the prediction models, and add a random disturbance parameter into an embedding layer of at least one of the prediction models; and
an operation module configured to perform voting operation on multiple prediction results to obtain a final prediction result of the pair of similar questions to be predicted;
wherein each of the prediction models comprises a plurality of prediction sub-models, wherein each of the plurality of prediction sub-models is obtained by training the prediction model from a specific training sample set of the pair of similar questions and a training sample set of the pair of similar questions determined by an allocation function;
the input module being further configured to input the pair of similar questions to be predicted into multiple prediction sub-models comprised in each of the prediction models to obtain a prediction sub-result output by each of the prediction sub-models, and performs voting operation on multiple prediction sub-results to obtain the prediction results.

### Claim 11 (depends on 10)

The device according to claim 10, wherein the prediction sub-model is trained by the steps of:
obtaining an original training sample set of the pair of similar questions;
performing a training sample extension processing on the original training sample set of the pair of similar questions by utilizing a similarity transmission principle to obtain an extended training sample set of the pair of similar questions;
determining the training sample set of the pair of similar questions from the extended training sample set of the pair of similar questions based on the allocation function;
training the prediction model by utilizing the training sample set of the pair of similar questions and the specific training sample set of the pair of similar questions to obtain the prediction sub-model; and
after the extended training sample set of the pair of similar questions is obtained, sequentially labeling each pair of training samples of the pair of similar questions in the extended training sample set of the pair of similar questions.

### Claim 12 (depends on 11)

The device according to claim 11, wherein the step of determining the training sample set of the pair of similar questions from the extended training sample set of the pair of similar questions based on the allocation function comprises:
determining a first label from the extended training sample set of the pair of similar questions by utilizing a first function of the allocation function;
determining a second label from the extended training sample set of the pair of similar questions based on the first label by utilizing a second function of the allocation function; and
selecting an extended training sample set of the pair of similar questions between the first label and the second label as the training sample set of the pair of similar questions.

### Claim 13 (depends on 1)

An electronic equipment comprising a processor and a memory, the memory storing computer executable instructions executable by the processor, and the processor executing the computer executable instructions to implement the method according to claim 1.