TosT5

The official implementation of the paper "Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training" (CIKM 2024).

Environment setup

Create an environment using Python 3.7, and install the dependencies with

pip install -r requirements.txt

Dataset preparation

Use pre-processed datasets

The pre-processed datasets that can be used directly for training has been placed under the folder transform/transformers_cache/downloads. And we recommend you to use them.

• LC-QuAD2.0-master: Processed LC-QuAD 2.0 dataset for fine-tuning;
• LC-QuAD2.0-pre: Processed LC-QuAD 2.0 dataset for pre-training;
• QALD_9_PULS: Processed Qald-9-plus dataset for fine-tuning;
• QALD_10: Processed Qald-10 dataset for fine-tuning.

Do yourself

You can also download the original datasets and process them yourself.

• LC-QuAD 2.0, link
• Qald-9-plus, link
• Qald-10, link

The preprocessing scripts are under the folder preprocess/LC-QuAD2.0-pre.

Pre-training

The configs/train_1.json is an example of parameter configuration for pre-training.

Replace "model_name_or_path" with the model name (t5-small, t5-base, or t5-large) or the path to your checkpoint , "output_dir" with where you want to store your outputs, and "cache_dir" with the place for caching.

You can simply run the code below:

CUDA_VISIBLE_DEVICES=0 python seq2seq/run_seq2seq.py configs/train_1.json

Fine-tuning

The configs/train_2.json is an example of parameter configuration for fine-tuning the model.

You should replace "dataset" with the name of the dataset that your want to fine-tune the model on, and you can choose from [lc_quad_2, qald_9, qald_10]. Replace "model_name_or_path" with the path to your checkpoint obtained during the previous pre-training.

You can simply run the code below:

CUDA_VISIBLE_DEVICES=0 python seq2seq/run_seq2seq.py configs/train_2.json

Citation

If you found the provided code with our paper useful in your work, we kindly request that you cite our work.

@inproceedings{su2024enhancing,
    title={Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training},
    author={Chang Su and Jiexing Qi and He Yan and Kai Zou and Zhouhan Lin},
    booktitle={33rd ACM International Conference on Information and Knowledge Management},
    year={2024}
}