This is the implementation of our EMNLP 2022 paper:

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks.

Please cite our paper when you use this code in your work.

Dependency

❱❱❱ pip install -r requirements.txt

Use our finetuned GPT-2

If you want to quickly generate metapaths with our model, please put the files in pretrained_models in this directory and run the generation script gen_metapath.sh . The k-hop meta-paths and their scores will be stored in meta_path_ft_heterographine_k.txt.

Finetune the GPT-2 yourself

Data preparation

Generate the masked data for finetuning GPT-2 for text infilling:

❱❱❱ python mask_data.py

Finetuning GPT-2 for text infilling

1. Follow the paper "Enabling language models to fill in the blanks"¹ to set the environment, and put their finetuned model on arxiv abstracts under abs_ilm.

2. train.sh is the script for finetuning the GPT-2 on HeteroGraphine.

Node Type Classifier Training

train_classifier.sh is the script for training the node type classifier for HeteroGraphine.

Meta-Path Generation

Remove the "--from-pretrained" in gen_metapath.sh and run it.

Acknowledgement

We use the open-source code of "Enabling language models to fill in the blanks"¹ to finetune the GPT-2 for text infilling

• [1] Donahue C, Lee M, Liang P. Enabling Language Models to Fill in the Blanks[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 2492-2501.