Skip to content

Latest commit

 

History

History
129 lines (115 loc) · 5.77 KB

README.md

File metadata and controls

129 lines (115 loc) · 5.77 KB
Raw

MultiHiertt

Data and code for ACL 2022 paper "MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data" https://aclanthology.org/2022.acl-long.454/

Requirements

  • python 3.9.7
  • pytorch 1.10.2,
  • pytorch-lightning 1.5.10
  • huggingface transformers 4.18.0
  • run pip install -r requirements.txt to install rest of the dependencies

Leaderboard

  • The leaderboard for the private test data is held on CodaLab

Main Files Structures

dataset/
training_configs/ & inference_configs/ # Configuration files for training and inference
lightning_modules/: 
    models/ # Implementation for each module
    datasets/ # Dataloaders
    callbacks/ # Callbacks for saving predictions
utils/ # Utilities for modules
txt_files/ # Txt files such as constant_list.txt, etc
output/ # Predictions and intermediate results
checkpoint/

convert_retriever_result.py # convert inference of Fact Retrieving & Question Type Classification Module into model input of Reasoning Modules.
trainer.py
evaluate.py

Dataset

The dataset is stored as json files Download Link, each entry has the following format:

"uid": unique example id;
"paragraphs": the list of sentences in the document;
"tables": the list of tables in HTML format in the document;
"table_description": the list of table descriptions for each data cell in tables. Generated by the pre-processing script;
"qa": {
  "question": the question;
  "answer": the answer;
  "program": the reasoning program;
  "text_evidence": the list of indices of gold supporting text facts;
  "table_evidence": the list of indices of gold supporting table facts;
}

MT2Net

We provide the model checkpoints in Hugging Face. Download them (*.ckpt) into the directory checkpoints.

1. Fact Retrieving & Question Type Classification Module

1.1 Training Stage

  • Edit training_configs/retriever_finetuning.yaml & training_configs/question_classification_finetuning.yaml to set your own project and data path.
  • Run the following commands to train the model.
export PYTHONPATH=`pwd`; python trainer.py {fit, validate} --config training_configs/*_finetuning.yaml

1.2 Inference Stage

  • Edit inference_configs/retriever_inference.yaml & inference_configs/retriever_inference.yaml to set your own project and data path.
  • Run the following commands to get the intermediate results for {Train, Dev, Test} set, respectively.
export PYTHONPATH=`pwd`; python trainer.py predict --ckpt_path checkpoints/*_model.ckpt --config inference_configs/*_inference.yaml

where checkpoints/*_model.ckpt can be replaced by the checkpoint path from training stage. And the inference set or files should be specified in *_inference.yaml.

2. Reasoning Module Input Generation

  • Prepare output/retriever_output/{train, dev, test}.json & output/question_classification_output/{train, dev, test}.json from Step 1.

  • Run the following commands to convert predictions of Fact Retrieving & Question Type Classification Module for {Train, Dev, Test} into model input of Reasoning Module, respectively.

python convert_retriever_result.py

The output files are stored in dataset/reasoning_module_input, where *_training.json is used for the training stage and *_inference.json is used for the inference stage.

3. Reasoning Module

3.1 Training Stage

  • Edit training_configs/program_generation_finetuning.yaml & training_configs/span_selection_finetuning.yaml to set your own project and data path.
  • Run the following commands to train the model and generate the prediction files.
export PYTHONPATH=`pwd`; python trainer.py fit --config training_configs/*_finetuning.yaml

3.2 Inference Stage

  • Edit inference_configs/program_generation_inference.yaml & inference_configs/span_selection_inference.yaml to set your own project and data path.
  • Run the following commands to get the prediction file for {Dev, Test} set
export PYTHONPATH=`pwd`; python trainer.py predict --ckpt_path checkpoints/*_model.ckpt --config inference_configs/*_inference.yaml

where checkpoints/*_model.ckpt can be replaced by the checkpoint path from training stage. And the inference set or files should be specified in *_inference.yaml.

Evaluation

Run the following commands to get the prediction file for {Dev, Test} set (and the performance on the Dev set), respectively.

python evaluate.py dataset/{test, dev}.json

The prediction file with the following format will be generated in the directory output/final_predictions:

[
    {
        "uid": "bd2ce4dbf70d43e094d93d314b30bd39",
        "predicted_ans": "106.0",
        "predicted_program": []
    },
    ...
]

For test set, Please zip the generated test prediction file test_predictions.json into test_predictions.zip; and submit test_predictions.zip to CodaLab to get the final score. Please exactly match the filename.

Any Questions?

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

Citation

@inproceedings{zhao-etal-2022-multihiertt,
    title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data",
    author = "Zhao, Yilun  and
      Li, Yunxiang  and
      Li, Chenying  and
      Zhang, Rui",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.454",
    pages = "6588--6600",
}