TEA

Code for "From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment", Findings of ACL 2023.

Dependencies

• python 3.9
• pytorch 1.12.1
• transformers 4.24.0
• tqdm

Dataset

You can download the DBP15K and SRPRS dataset from JAPE, RSN, or SDEA.

1. Unzip the datasets in TEA/data.
2. Preprocess the datasets.

cd src
python DBPPreprocess.py
python SRPRSPreprocess.py

Pre-trained Language Model

You can download the pre-trained language model bert-base-multilingual-uncased from huggingface and put the model in TEA/pre_trained_models

Project Structure

TEA/
├── src/: The soruce code. 
├── data/: The datasets. 
│   ├── DBP15k/: The downloaded DBP15K benchmark. 
│   │   ├── fr_en/
│   │   ├── ja_en/
│   │   ├── zh_en/
│   ├── entity-alignment-full-data/: The downloaded SRPRS benchmark. 
│   │   ├── en_de_15k_V1/
│   │   ├── en_fr_15k_V1/
├── pre_trained_models/: The pre-trained transformer-based models. 
│   ├── bert-base-multilingual-uncased: The model used in our experiments.
│   │   ├── config.json
│   │   ├── pytorch_model.bin
│   │   ├── tokenizer.json
│   │   ├── tokenizer_config.json
│   │   ├── vocab.txt
│   ├── ......

How to run

To run TEA, use the example script run_dbp15k.sh or run_srprs.sh. You could customize the following parameters:

• --nsp: trains with TEA-NSP. If not specified, the default is TEA-MLM.
• --neighbor: adds neighbor sequences in training. If not specified, the model is only trained with attribute sequences.
• --template_id: changes the template of prompt according to the template_list in src/KBConfig.py.

You could also run FT-EA with src/FTEATrain.py.

Acknowledgements

Our codes are modified based on SDEA. We would like to appreciate their open-sourced work.

Citation

Please cite the following paper as reference if you find our work useful.

@inproceedings{zhao-etal-2023-alignment,
    title = "From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment",
    author = "Zhao, Yu  and
      Wu, Yike  and
      Cai, Xiangrui  and
      Zhang, Ying  and
      Zhang, Haiwei  and
      Yuan, Xiaojie",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.559",
    pages = "8795--8806",
}