GitHub - lingochamp/Multi-Scale-BERT-AES: Demo for the paper "On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation"

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Latest commit yongjie.wang up readme May 3, 2022 e114848 · May 3, 2022 History 2 Commits
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Readme	Readme	up readme	May 3, 2022
asap.ini	asap.ini	first commit	May 3, 2022
data.py	data.py	first commit	May 3, 2022
decode.sh	decode.sh	first commit	May 3, 2022
document_bert_architectures.py	document_bert_architectures.py	first commit	May 3, 2022
encoder.py	encoder.py	first commit	May 3, 2022
evaluate.py	evaluate.py	first commit	May 3, 2022
model_architechure_bert_multi_scale_multi_loss.py	model_architechure_bert_multi_scale_multi_loss.py	first commit	May 3, 2022
pred.txt	pred.txt	first commit	May 3, 2022
predict_multi_scale_multi_loss.py	predict_multi_scale_multi_loss.py	first commit	May 3, 2022
requirements.txt	requirements.txt	first commit	May 3, 2022

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This is a demo for the paper(NAACL 2022 long paper accepted): "On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation"

Steps to run the decoding process:
step 1: Download the project to the computer.
step 2: Download the model(for ASAP's prompt 8) to the computer with the following link:
        Link: https://pan.baidu.com/s/1_m_-DQlX-dLh1XdhOMzj1A?pwd=tmmb
step 3: Update the config file "asap.ini" and set the parameters as following:
        data_dir: your local directory to store the essays of prompt 8 (file format: id \t text \t score);
        model_directory: your local directory to store the model which is obtained in step 2;
        result_file: the path of file to store prediction result.
step 4: Run the following script to get scoring result:
        sh decode.sh