Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems

Yanyan Zou, Wei Lu


Abstract
An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, Quantity Tagger, that automatically discovers such hidden relations by tagging each quantity with a sign corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized quantity span surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.
Anthology ID:
P19-1517
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5246–5251
Language:
URL:
https://aclanthology.org/P19-1517
DOI:
10.18653/v1/P19-1517
Bibkey:
Cite (ACL):
Yanyan Zou and Wei Lu. 2019. Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5246–5251, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems (Zou & Lu, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1517.pdf
Supplementary:
 P19-1517.Supplementary.pdf
Code
 zoezou2015/quantity_tagger