Generating Equation by Utilizing Operators : GEO model

Kyung Seo Ki, Donggeon Lee, Bugeun Kim, Gahgene Gweon

Abstract
Math word problem solving is an emerging research topic in Natural Language Processing. Recently, to address the math word problem-solving task, researchers have applied the encoder-decoder architecture, which is mainly used in machine translation tasks. The state-of-the-art neural models use hand-crafted features and are based on generation methods. In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use hand-crafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge. To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction. To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer. Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1% in MAWPS, and 62.5% in DRAW-1K, and reached comparable performance of 82.1% in ALG514 dataset.
Anthology ID:
2020.coling-main.38
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
426–436
Language:
URL:
https://aclanthology.org/2020.coling-main.38
DOI:
10.18653/v1/2020.coling-main.38
Bibkey:
Cite (ACL):
Kyung Seo Ki, Donggeon Lee, Bugeun Kim, and Gahgene Gweon. 2020. Generating Equation by Utilizing Operators : GEO model. In Proceedings of the 28th International Conference on Computational Linguistics, pages 426–436, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Generating Equation by Utilizing Operators : GEO model (Ki et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.38.pdf
Data
ALG514, DRAW-1K, MAWPS
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