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.", }
<?xml version="1.0" encoding="UTF-8"?> <modsCollection xmlns="http://www.loc.gov/mods/v3"> <mods ID="chen-etal-2022-teaching-neural"> <titleInfo> <title>Teaching Neural Module Networks to Do Arithmetic</title> </titleInfo> <name type="personal"> <namePart type="given">Jiayi</namePart> <namePart type="family">Chen</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Xiao-Yu</namePart> <namePart type="family">Guo</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Yuan-Fang</namePart> <namePart type="family">Li</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Gholamreza</namePart> <namePart type="family">Haffari</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2022-10</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Proceedings of the 29th International Conference on Computational Linguistics</title> </titleInfo> <name type="personal"> <namePart type="given">Nicoletta</namePart> <namePart type="family">Calzolari</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Chu-Ren</namePart> <namePart type="family">Huang</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Hansaem</namePart> <namePart type="family">Kim</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">James</namePart> <namePart type="family">Pustejovsky</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Leo</namePart> <namePart type="family">Wanner</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Key-Sun</namePart> <namePart type="family">Choi</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Pum-Mo</namePart> <namePart type="family">Ryu</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Hsin-Hsi</namePart> <namePart type="family">Chen</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Lucia</namePart> <namePart type="family">Donatelli</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Heng</namePart> <namePart type="family">Ji</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Sadao</namePart> <namePart type="family">Kurohashi</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Patrizia</namePart> <namePart type="family">Paggio</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Nianwen</namePart> <namePart type="family">Xue</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Seokhwan</namePart> <namePart type="family">Kim</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Younggyun</namePart> <namePart type="family">Hahm</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Zhong</namePart> <namePart type="family">He</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Tony</namePart> <namePart type="given">Kyungil</namePart> <namePart type="family">Lee</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Enrico</namePart> <namePart type="family">Santus</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Francis</namePart> <namePart type="family">Bond</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Seung-Hoon</namePart> <namePart type="family">Na</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>International Committee on Computational Linguistics</publisher> <place> <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm> </place> </originInfo> <genre authority="marcgt">conference publication</genre> </relatedItem> <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.</abstract> <identifier type="citekey">chen-etal-2022-teaching-neural</identifier> <location> <url>https://aclanthology.org/2022.coling-1.129</url> </location> <part> <date>2022-10</date> <extent unit="page"> <start>1502</start> <end>1510</end> </extent> </part> </mods> </modsCollection>
%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.