Abstract
Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning. Neural Module Networks (NMNs), follow the programmer-interpreter framework and design trainable modules to learn different reasoning skills. However, NMNs only have limited reasoning abilities, and lack numerical reasoning capability. We upgrade NMNs by: (a) bridging the gap between its interpreter and the complex questions; (b) introducing addition and subtraction modules that perform numerical reasoning over numbers. On a subset of DROP, experimental results show that our proposed methods enhance NMNs’ numerical reasoning skills by 17.7% improvement of F1 score and significantly outperform previous state-of-the-art models.- Anthology ID:
- 2022.coling-1.129
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1502–1510
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.129
- DOI:
- Bibkey:
- Cite (ACL):
- Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li, and Gholamreza Haffari. 2022. Teaching Neural Module Networks to Do Arithmetic. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1502–1510, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Teaching Neural Module Networks to Do Arithmetic (Chen et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.129.pdf
- Data
- DROP
Export citation
@inproceedings{chen-etal-2022-teaching-neural,
title = "Teaching Neural Module Networks to Do Arithmetic",
author = "Chen, Jiayi and
Guo, Xiao-Yu and
Li, Yuan-Fang and
Haffari, Gholamreza",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.129",
pages = "1502--1510",
abstract = "Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning. Neural Module Networks (NMNs), follow the programmer-interpreter framework and design trainable modules to learn different reasoning skills. However, NMNs only have limited reasoning abilities, and lack numerical reasoning capability. We upgrade NMNs by: (a) bridging the gap between its interpreter and the complex questions; (b) introducing addition and subtraction modules that perform numerical reasoning over numbers. On a subset of DROP, experimental results show that our proposed methods enhance NMNs{'} numerical reasoning skills by 17.7{\%} improvement of F1 score and significantly outperform previous state-of-the-art models.",
}
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%0 Conference Proceedings %T Teaching Neural Module Networks to Do Arithmetic %A Chen, Jiayi %A Guo, Xiao-Yu %A Li, Yuan-Fang %A Haffari, Gholamreza %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F chen-etal-2022-teaching-neural %X Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning. Neural Module Networks (NMNs), follow the programmer-interpreter framework and design trainable modules to learn different reasoning skills. However, NMNs only have limited reasoning abilities, and lack numerical reasoning capability. We upgrade NMNs by: (a) bridging the gap between its interpreter and the complex questions; (b) introducing addition and subtraction modules that perform numerical reasoning over numbers. On a subset of DROP, experimental results show that our proposed methods enhance NMNs’ numerical reasoning skills by 17.7% improvement of F1 score and significantly outperform previous state-of-the-art models. %U https://aclanthology.org/2022.coling-1.129 %P 1502-1510
Markdown (Informal)
[Teaching Neural Module Networks to Do Arithmetic](https://aclanthology.org/2022.coling-1.129) (Chen et al., COLING 2022)
- Teaching Neural Module Networks to Do Arithmetic (Chen et al., COLING 2022)
ACL
- Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li, and Gholamreza Haffari. 2022. Teaching Neural Module Networks to Do Arithmetic. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1502–1510, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.