@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",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
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",
doi = "10.18653/v1/2022.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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems
%A Xu, Jialiang
%A Zhou, Mengyu
%A He, Xinyi
%A Han, Shi
%A Zhang, Dongmei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F xu-etal-2022-towards-robust
%X 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.
%R 10.18653/v1/2022.emnlp-main.542
%U https://aclanthology.org/2022.emnlp-main.542
%U https://doi.org/10.18653/v1/2022.emnlp-main.542
%P 7950-7966
Markdown (Informal)
[Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems](https://aclanthology.org/2022.emnlp-main.542) (Xu et al., EMNLP 2022)
ACL