BDBC-KG-NLP / WWW2021_USAL

Source code of the WWW2021 paper "Unsupervised Semantic Association Learning with Latent Label Inference".

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USAL

This is a pytorch implementation of the WWW 2021 paper "Unsupervised Semantic Association Learning with Latent Label Inference".

Requirements

python3.6.0+

pytorch 1.7.0+

transformers

tensorboard

Usage

Download the data, and change the datapath in the configs file.

To train a new model, run the following command:

python main.py --dataset='config name' --main=wsd.py/qa.py --do_train

Citation

If this work is helpful, please cite as:

@inproceedings{zhang2021unsupervised,
  title={Unsupervised Semantic Association Learning with Latent Label Inference},
  author={Zhang, Yanzhao and Zhang, Richong and Kim, Jaein and Liu, Xudong and Mao, Yongyi},
  booktitle={Proceedings of the Web Conference 2021},
  pages={4010--4019},
  year={2021}
}

License

MIT

About

Source code of the WWW2021 paper "Unsupervised Semantic Association Learning with Latent Label Inference".


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Language:Python 100.0%