Learning From How Human Correct

• URL: http://arxiv.org/abs/2102.00225v1
• Date: Sat, 30 Jan 2021 13:13:50 GMT
• Title: Learning From How Human Correct
• Authors: Tong Guo
• Abstract summary: 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, while we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model.
• Score: 0.685316573653194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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