scholarly journals Achieving 90% In Data-Centric Industry Deep Learning Task

2022 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Noisy Data ◽  
Learning Task ◽  
Simple Method ◽  
Sequence Generation ◽  
Learning Tasks ◽  
Human Evaluation ◽  
Model Predictions ◽  
Click Through Rate

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The experimental results and human evaluation results verify our idea.

scholarly journals Achieving 90% In Data-Centric Industry Deep Learning Task

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Noisy Data ◽  
Learning Task ◽  
Simple Method ◽  
Sequence Generation ◽  
Learning Tasks ◽  
Human Evaluation ◽  
Model Predictions ◽  
Click Through Rate

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The experimental results and human evaluation results verify our idea.

Achieving 90% In Data-Centric Industry Deep Learning Task

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Noisy Data ◽  
Learning Task ◽  
Simple Method ◽  
Sequence Generation ◽  
Learning Tasks ◽  
Human Evaluation ◽  
Model Predictions ◽  
Click Through Rate

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The experimental results and human evaluation results verify our idea.

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From Human Correction For Data-Centric Deep Learning

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

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