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Any-Shot Data-to-Text

ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Model
Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing, Zhiting Hu
Accepted to Findings of EMNLP 2022

Setting Up

  • Python == 3.8

Install the dependencies by

pip install -r requirements.txt

Download Weakly-Supervised Checkpoint

We release our ASDOT model that is finetuned with the weakly-supervision dataset WikiFluent in Google Drive. This model can be used for zero-/few-shot settings.

Usage

WebNLG and DART is in data/webnlg and data/dart, respectively.
In the datasets, we have already disambiguated the triples with the GPT-3 generated templates in temp.json.

We use the WebNLG dataset as the example for usage illustration in the following sections.

Sample

For few-shot experiments, you may first sample the dataset by:

python sample.py \
  --input data/webnlg/train.json \
  --output data/webnlg/train_20.json \
  --sample 20

This command generates a training set that contains 20 samples.

Training

python run.py \
  --train_file data/webnlg/train.json \
  --valid_file data/webnlg/val.json \
  --dataset_name webnlg \
  --model_name_or_path t5-small \
  --source_prefix "summarize: " \
  --input_column input \
  --per_device_train_batch_size 4 \
  --per_device_eval_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 40 \
  --output_dir output/ \
  --logging_steps 100 \
  --no_progress_bar \
  --num_beams 1

Please use python run.py --help to see the usage of the arguments. If you want to train a baseline model (with linearized data as input), please set --source_prefix "translate Graph to English:" --input_column triple --add_special_tokens.

Evaluation

python run.py \
  --valid_file data/webnlg/test_both.json \
  --dataset_name webnlg \
  --model_name_or_path output/checkpoint_best \
  --source_prefix "summarize: " \
  --per_device_eval_batch_size 12 \
  --output_dir output/ \
  --num_beams 5 \
  --do_eval

This command reports BLEU, METEOR and PARENT-F1 of the checkpoint on the corresponding test set. WebNLG has three test sets: test_both, test_seen, and test_unseen. DART has test_both and test_unseen.

Inference / Generation

python run.py \
  --valid_file data/webnlg/test_both.json \
  --dataset_name webnlg \
  --model_name_or_path output/checkpoint_best \
  --source_prefix "summarize: " \
  --per_device_eval_batch_size 12 \
  --output_dir output/ \
  --num_beams 5 \
  --do_gen