eirazhang / laco

This repository contains the code for our paper [Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning]

#Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning This repository contains the code for the ACL 2021 paper

"Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning".

If you use LACO in your work, please cite it as follows:

@article{zhang2021enhancing,
  title={Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning},
  author={Zhang, Ximing and Zhang, Qian-Wen and Yan, Zhao and Liu, Ruifang and Cao, Yunbo},
  journal={arXiv preprint arXiv:2106.03103},
  year={2021}
}

##Settings Environment Requirements

• python 3.6+

• Tensorflow 1.12.0+

Environmental preparation

• You can change the experimental settings in LACO/common/global_config.py

• The initial content under directory LACO/ie/src/bert is primarily from Google bert. Citation information is recorded in the corresponding file. You can download and unzip it at LACO/pretrained_model/ .

##Datasets

• AAPD
• RCV1-V2

Data Preparation

The sample data are in the directory LACO/log/re_model/input. Note that the "text" field stores the text content, the "spo_list" field stores the relevant labels in "predicate", and the other fields can be ignored.

##How To Run

• Train_mltc_with_plcp: python ie/train_main_plcp.py
• Test_mltc_with_plcp: python ie/test_main_plcp.py
• Train_mltc_with_clcp: python ie/train_main_clcp.py
• Test_mltc_with_clcp: python ie/test_main_clcp.py

##Results The best model of +PLCP of AAPD dataset and and RCV1V2 dataset can be found at https://share.weiyun.com/5EXHqEPE (password: 8yrgji) for your reference.

##© Copyright Ximing Zhang ([email protected]), Qian-Wen Zhang ([email protected]), Zhao Yan ([email protected]), Ruifang Liu ([email protected]), Yunbo Cao ([email protected]), Tencent Cloud Xiaowei, Beijing, China && Beijing University of Posts and Telecommunications, Beijing, China

This code package can be used freely for academic, non-profit purposes. For other usage, please contact us for further information (Ximing Zhang: [email protected]).