Investigating Pretrained Language Models for Graph-to-Text Generation

This repository contains the code for the paper: "Investigating Pretrained Language Models for Graph-to-Text Generation".

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

This project is implemented using the framework HuggingFace. Please, refer to their websites for further details on the installation and dependencies.

Environments and Dependencies

python 3.6
transformers 3.3.1
pytorch-lightning 0.9.0
torch 1.4.0
parsimonious 0.8.1

Datasets

In our experiments, we use the following datasets: AMR17, WebNLG and AGENDA.

Preprocess

First, convert the dataset into the format required for the model.

For the AMR17, run:

./preprocess_AMR.sh <dataset_folder>

For the WebNLG, run:

./preprocess_WEBNLG.sh <dataset_folder>

For the AGENDA, run:

./preprocess_AGENDA.sh <dataset_folder>

Finetuning

For finetuning the models using the AMR dataset, execute:

./finetune_AMR.sh <model> <gpu_id>

For the WebNLG dataset, execute:

./finetune_WEBNLG.sh <model> <gpu_id>

For the AGENDA dataset, execute:

./finetune_AGENDA.sh <model> <gpu_id>

Options for <model> are t5-small, t5-base, t5-large, facebook/bart-base or facebook/bart-large.

Example:

./finetune_AGENDA.sh t5-small 0

Decoding

For decoding, run:

./decode_AMR.sh <model> <checkpoint> <gpu_id>
./decode_WEBNLG.sh <model> <checkpoint> <gpu_id>
./decode_AGENDA.sh <model> <checkpoint> <gpu_id>

Example:

./decode_WEBNLG.sh t5-base webnlg-t5-base.ckpt 0

Trained models

AMR17
bart-base - BLEU: 36.71 (output)
bart-large - BLEU: 43.47 (output)
t5-small - BLEU: 38.45 (output)
t5-base - BLEU: 42.54 (output)
t5-large - BLEU: 45.80 (output)

WebNLG
bart-base - All BLEU: 53.11 (output), Seen BLEU: 62.74 (output), Unseen BLEU: 41.53 (output)
bart-large - All BLEU: 54.72 (output), Seen BLEU: 63.45 (output), Unseen BLEU: 43.97 (output)
t5-small - All BLEU: 56.34 (output), Seen BLEU: 65.05 (output), Unseen BLEU: 45.37 (output)
t5-base - All BLEU: 59.17 (output), Seen BLEU: 64.64 (output), Unseen BLEU: 52.55 (output)
t5-large - All BLEU: 59.70 (output), Seen BLEU: 64.71 (output), Unseen BLEU: 53.67 (output)

* BLEU values for AMR17 are calculated using sacreBLEU in detok outputs. BLEU values for WebNLG are calculated using tok outputs using the challange's script, that uses multi-bleu.perl.

For more details regading hyperparameters, please refer to HuggingFace.

Contact person: Leonardo Ribeiro, ribeiro@aiphes.tu-darmstadt.de

Citation

@misc{ribeiro2020investigating,
      title={Investigating Pretrained Language Models for Graph-to-Text Generation}, 
      author={Leonardo F. R. Ribeiro and Martin Schmitt and Hinrich Schütze and Iryna Gurevych},
      year={2020},
      eprint={2007.08426},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

dengjianbo3/plms-graph2text