DNC Framework

Code and data for the EMNLP 2022 paper Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems.

The DNC (Diagnosing Numerical Capability) framework is proposed to probe the robustness in systems on Question Answering datasets that require numerical reasoning capabilities.

Four numerical capabilities, stemming from the two solving stages of numerical QA questions, are highlighted. Accordingly, eight perturbations are designed to probe these capabilities. Being trivial to humans, these perturbations are expected not to affect the system performance significantly.

Empirical Results show that current systems are errorprone on the perturbed test set (“Attack”), and demonstrate nontrivial performance drop even trained on the perturbed training set (“Defense”).

Datasets & Models

Datasets

• For all datasets, we refer users to original sources for the dataset files.

Dataset	Source	Original Paper	Comment
ASDiv-a	https://github.com/LYH-YF/MWPToolkit	(Miao et al., 2020)
DROP	https://allenai.org/data/drop	(Dua et al., 2019)	DROP-num filtered by us
TATQA	https://nextplusplus.github.io/TAT-QA/	(Zhu et al., 2022)	TATQA-a filtered by us

• We provide the fitlered DROP-num, TATQA-a and the manually curated Logic Attack challenge set for ASDiv-a in data/dataset/.

data
│
├─dataset
│ ├─asdiv-a-manual
│ │ testset.json
│ │ trainset.json
│ │ validset.json
│ │
│ ├─drop-num
│ │ testset.json
│ │ trainset.json
│ │ validset.json
│ │
│ └─tatqa-a
│ testset.json
│ trainset.json
│ validset.json
├─number_tokenizer
└─perturbation

• We also provide the perturbing scripts we used to create the Attack datasets and the Defense datasets in data/perturbation/, which can be called with

python -m data.perturbation.asdiv_a.asdiv_a_auto
python -m data.perturbation.drop_num.drop_num_auto
python -m data.perturbation.tatqa.tatqa_auto

Models

• For T5 and BART, we provide the scripts for experiments with pytorch-lightning.

code
│
├─Gen_DROP
│ config.py
│ drop_dataset.py
│ drop_model.py
│ exp.py
│
└─Gen_MWP
asdiv_dataset.py
asdiv_model.py
config.py
exp.py

• The can be called with

export CUDA_VISIBLE_DEVICES=0 # or more gpus, lightning automatically handles themexport SEED=<your_seed>export MODEL_NAME=bart # or t5export DATASET=asdiv-a # or drop-num / tatqa-aexport SETTING=atk # or defexport EPOCH=<your_epoch_number>
python -m Gen_MWP.exp \
--seed ${SEED} \
--model_name ${MODEL_NAME} \
--root_dataset_name ${DATASET} \
--setting_name ${SETTING} \
--max_epoch ${EPOCH}

• For GPT2, Graph2Tree, and TagOps, please refer to their existing implementation.

Model	Source	Original Paper
GPT2	https://github.com/LYH-YF/MWPToolkit	(Radford et al., 2019)
Graph2Tree	https://github.com/LYH-YF/MWPToolkit	(Zhang et al., 2020)
TagOps	https://github.com/NExTplusplus/TAT-QA	(Zhu et al., 2022)

Dependency

We assume the usage of conda as the environment management tool.

conda create -n dnc python=3.9
conda activate dnc
pip install torch --extra-index-url https://download.pytorch.org/whl/cu116
pip install -U mwptoolkit pytorch-lightning
pip install -U -r requirements.txt

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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Trademarks

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Reference

If you find our work useful for your research, please consider citing

@inproceedings{xu-etal-2022-towards-robust,
title = "Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of {NLP} Systems",
author = "Xu, Jialiang and
Zhou, Mengyu and
He, Xinyi and
Han, Shi and
Zhang, Dongmei",
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.542",
pages = "7950--7966",
abstract = "Numerical Question Answering is the task of answering questions that require numerical capabilities. Previous works introduce general adversarial attacks to Numerical Question Answering, while not systematically exploring numerical capabilities specific to the topic. In this paper, we propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. A series of numerical capabilities are highlighted, and corresponding dataset perturbations are designed. Empirical results indicate that existing systems are severely challenged by these perturbations. E.g., Graph2Tree experienced a 53.83{\%} absolute accuracy drop against the {``}Extra{''} perturbation on ASDiv-a, and BART experienced 13.80{\%} accuracy drop against the {``}Language{''} perturbation on the numerical subset of DROP. As a counteracting approach, we also investigate the effectiveness of applying perturbations as data augmentation to relieve systems{'} lack of robust numerical capabilities. With experiment analysis and empirical studies, it is demonstrated that Numerical Question Answering with robust numerical capabilities is still to a large extent an open question. We discuss future directions of Numerical Question Answering and summarize guidelines on future dataset collection and system design.",
}