Papers › Generating Equation by Utilizing Operators : GEO model
Generating Equation by Utilizing Operators : GEO model
Kyung Seo Ki, Donggeon Lee, Bugeun Kim, Gahgene Gweon
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Math Word Problem Solving | ALG514 | GEO | Accuracy (%) | 82.1 | #2 of 10 | Archive leaderboard | report |
| Math Word Problem Solving | DRAW-1K | GEO | Accuracy (%) | 62.5 | #2 of 5 | Archive leaderboard | report |
| Math Word Problem Solving | MAWPS | GEO | Accuracy (%) | 85.1 | #12 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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