ReasTAP

The code for EMNLP 2022 paper ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples.

Prepare Environment

We officially support python 3.9. You could use following commands to install the required packages

pip install -r requirements.txt

Synthetic Table QA Generation

We provide details of the synthetic table QA generation pipeline in the README.md file located in the synthetic_tableqa_generation folder, along with the pretraining data and intermediate files shared on Google Drive.

Pretraining Data Format

Each example contains 3 fields source, reasoning_type, question, table and answers, where

• source indicates whether the example is from the synthetic table QA generation pipeline or the SQL execution data generation pipeline (from tapex).
• reasoning_type indicates the type of table reasoning skills required to answer the question, if the example is from the synthetic table QA generation pipeline.
• table is a 2-dimensional table with a header and one or multiple rows.
• question is the natural language question or SQL query.
• answers is a list of answers or executed results.

Experiments

Model list

We have released following models in Huggingface Hub.

• Pretrained models
  • Yale-LILY/reastap-large: Pretrained on synthetic table qa and SQL execution data.
• Finetuned models
  • Table Question Answering
    • Yale-LILY/reastap-large-finetuned-wtq: Yale-LILY/reastap-large finetuned on the WikiTableQuestions dataset.
    • Yale-LILY/reastap-large-finetuned-wikisql: Yale-LILY/reastap-large finetuned on the WikiSQL dataset.
  • Table Fact Checking
    • Yale-LILY/reastap-large-finetuned-tabfact: Yale-LILY/reastap-large finetuned on the TabFact dataset.
  • Table-to-Text Generation
    • Yale-LILY/reastap-large-finetuned-logicnlg: Yale-LILY/reastap-large finetuned on the LogicNLG dataset.

We have prepared bash scripts for both pretraining and finetuning in the bash_scripts folder. Please ensure that you adjust and set CUDA_VISIBLE_DEVICES and batch size accordingly in each bash script. The current setting works for an 8x A6000-48G cluster with 1024G memory.

Pretraining Experiments

bash bash_scripts/train_scripts/pretrain.sh

Fintuning Experiments

Training

bash_scripts/train_scripts/train_*.sh

Evaluation

bash_scripts/evaluation_scripts/eval_*.sh

Inference

bash_scripts/prediction_scripts/predict_*.sh

The prediction files in json format will be stored in the outputs/*/ folder. To evaluate performance on the LogicNLG dataset, please use the evaluation scripts in their official LogicNLG github repo.

Results

To facilitate the reproducibility of our results by other researchers, we have rewritten the model code using the transformers library because it enables us to directly share the model weights on Huggingface Hub. However, due to this change, there might be slight differences in the results compared to the original paper, which employed the fairseq library. The updated results are as follows:

Task	Dev Accuracy	Test Accuracy
WikiSQL	89.6	89.2
WikiTableQuestion	59.7	58.7
TabFact	84.6	84.9

		BLEU-1/2/3	SP-Acc	NLI-Acc		BLEU-1/2/3	SP-Acc	NLI-Acc
LogicNLG	Dev	53.3/33.6/20.0	54.8	90.1	Test	53.7/34.3/20.7	54.4	89.3

The output files are stored in outputs folder.

Contact

For any issues or questions, kindly email us at: Yilun Zhao (yilun.zhao@yale.edu).

Citation

@inproceedings{zhao-etal-2022-reastap,
    title = "{R}eas{TAP}: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples",
    author = "Zhao, Yilun  and
      Nan, Linyong  and
      Qi, Zhenting  and
      Zhang, Rui  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.615",
    pages = "9006--9018",
}