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[EMNLP 2022 Findings] Search to Pass Messages for Temporal Knowledge Graph Completion

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Search to Pass Messages for Temporal Knowledge Graph Completion

emnlp paper arxiv


Overview

This repository contains the code for Search to Pass Messages for Temporal Knowledge Graph Completion in Findings of EMNLP 2022.

Requirements

python=3.8
torch==1.9.0+cu111
dgl+cu111==0.6.1

Instructions to run the experiment

Search process

# Random baseline
python main.py --train_mode search --search_mode random --encoder SPATune --max_epoch 200

# SPA supernet training
python main.py --train_mode search --search_mode spos --encoder SPASPOSSearch --search_max_epoch 800

# SPA architecture search
python main.py --train_mode search --search_mode spos_search --encoder SPASPOSSearch --arch_sample_num 1000 --weight_path <xx.pt>

Fine-tuning process

python main.py --train_mode tune --encoder SPATune --search_res_file <xxx.json>

Citation

Readers are welcomed to fork this repository to reproduce the experiments and follow our work. Please kindly cite our paper

@inproceedings{wang2022search,
    title={Search to Pass Messages for Temporal Knowledge Graph Completion},
    author={Wang, Zhen and Du, Haotong and Yao, Quanming and Li, Xuelong},
    booktitle={Findings of the Association for Computational Linguistics: EMNLP 2022},
    pages={6160--6172},
    year={2022}
}

Contact

If you have any questions, feel free to contact me at [email protected].

Acknowledgement

The codes of this paper are partially based on the codes of SANE and TeMP. We thank the authors of above work.

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