@inproceedings{chen-etal-2023-improving,
title = "Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task",
author = "Chen, Chung-Chi and
Takamura, Hiroya and
Kobayashi, Ichiro and
Miyao, Yusuke",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.4",
doi = "10.18653/v1/2023.findings-eacl.4",
pages = "69--77",
abstract = "Numbers have unique characteristics to words. Teaching models to understand numbers in text is an open-ended research question. Instead of discussing the required calculation skills, this paper focuses on a more fundamental topic: understanding numerals. We point out that innumeracy{---}the inability to handle basic numeral concepts{---}exists in most pretrained language models (LMs), and we propose a method to solve this issue by exploring the notation of numbers. Further, we discuss whether changing notation and pre-finetuning along with the comparing-number task can improve performance in three benchmark datasets containing quantitative-related tasks. The results of this study indicate that input reframing and the proposed pre-finetuning task is useful for RoBERTa.",
}
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<abstract>Numbers have unique characteristics to words. Teaching models to understand numbers in text is an open-ended research question. Instead of discussing the required calculation skills, this paper focuses on a more fundamental topic: understanding numerals. We point out that innumeracy—the inability to handle basic numeral concepts—exists in most pretrained language models (LMs), and we propose a method to solve this issue by exploring the notation of numbers. Further, we discuss whether changing notation and pre-finetuning along with the comparing-number task can improve performance in three benchmark datasets containing quantitative-related tasks. The results of this study indicate that input reframing and the proposed pre-finetuning task is useful for RoBERTa.</abstract>
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%0 Conference Proceedings
%T Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task
%A Chen, Chung-Chi
%A Takamura, Hiroya
%A Kobayashi, Ichiro
%A Miyao, Yusuke
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F chen-etal-2023-improving
%X Numbers have unique characteristics to words. Teaching models to understand numbers in text is an open-ended research question. Instead of discussing the required calculation skills, this paper focuses on a more fundamental topic: understanding numerals. We point out that innumeracy—the inability to handle basic numeral concepts—exists in most pretrained language models (LMs), and we propose a method to solve this issue by exploring the notation of numbers. Further, we discuss whether changing notation and pre-finetuning along with the comparing-number task can improve performance in three benchmark datasets containing quantitative-related tasks. The results of this study indicate that input reframing and the proposed pre-finetuning task is useful for RoBERTa.
%R 10.18653/v1/2023.findings-eacl.4
%U https://aclanthology.org/2023.findings-eacl.4
%U https://doi.org/10.18653/v1/2023.findings-eacl.4
%P 69-77
Markdown (Informal)
[Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task](https://aclanthology.org/2023.findings-eacl.4) (Chen et al., Findings 2023)
ACL