Learning to use formulas to solve simple arithmetic problems

Arindam Mitra; Chitta Baral

Learning to use formulas to solve simple arithmetic problems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Solving simple arithmetic word problems is one of the challenges in Natural Language Understanding. This paper presents a novel method to learn to use formulas to solve simple arithmetic word problems. Our system, analyzes each of the sentences to identify the variables and their attributes; and automatically maps this information into a higher level representation. It then uses that representation to recognize the presence of a formula along with its associated variables. An equation is then generated from the formal description of the formula. In the training phase, it learns to score the <formula, variables> pair from the systematically generated higher level representation. It is able to solve 86.07% of the problems in a corpus of standard primary school test questions and beats the state-of-the-art by a margin of 8.07%.

Original language	English (US)
Title of host publication	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
Publisher	Association for Computational Linguistics (ACL)
Pages	2144-2153
Number of pages	10
Volume	4
ISBN (Electronic)	9781510827585
State	Published - 2016
Event	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany Duration: Aug 7 2016 → Aug 12 2016

Other

Other	54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Country	Germany
City	Berlin
Period	8/7/16 → 8/12/16

ASJC Scopus subject areas

Language and Linguistics
Linguistics and Language

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Mitra, A., & Baral, C. (2016). Learning to use formulas to solve simple arithmetic problems. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (Vol. 4, pp. 2144-2153). Association for Computational Linguistics (ACL).

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abstract = "Solving simple arithmetic word problems is one of the challenges in Natural Language Understanding. This paper presents a novel method to learn to use formulas to solve simple arithmetic word problems. Our system, analyzes each of the sentences to identify the variables and their attributes; and automatically maps this information into a higher level representation. It then uses that representation to recognize the presence of a formula along with its associated variables. An equation is then generated from the formal description of the formula. In the training phase, it learns to score the pair from the systematically generated higher level representation. It is able to solve 86.07% of the problems in a corpus of standard primary school test questions and beats the state-of-the-art by a margin of 8.07%.",

author = "Arindam Mitra and Chitta Baral",

year = "2016",

language = "English (US)",

volume = "4",

pages = "2144--2153",

booktitle = "54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers",

publisher = "Association for Computational Linguistics (ACL)",

note = "54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 ; Conference date: 07-08-2016 Through 12-08-2016",

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AU - Mitra, Arindam

AU - Baral, Chitta

PY - 2016

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AB - Solving simple arithmetic word problems is one of the challenges in Natural Language Understanding. This paper presents a novel method to learn to use formulas to solve simple arithmetic word problems. Our system, analyzes each of the sentences to identify the variables and their attributes; and automatically maps this information into a higher level representation. It then uses that representation to recognize the presence of a formula along with its associated variables. An equation is then generated from the formal description of the formula. In the training phase, it learns to score the pair from the systematically generated higher level representation. It is able to solve 86.07% of the problems in a corpus of standard primary school test questions and beats the state-of-the-art by a margin of 8.07%.

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M3 - Conference contribution

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BT - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers

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