Papers › EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers
Bugeun Kim, Kyung Seo Ki, Sangkyu Rhim, Gahgene Gweon
In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem. To enhance the explainability of the encoding process of a neural model, EPT-X adopts the concepts of plausibility and faithfulness which are drawn from math word problem solving strategies by humans. A plausible explanation is one that includes contextual information for the numbers and variables that appear in a given math word problem. A faithful explanation is one that accurately represents the reasoning process behind the model’s solution equation. The EPT-X model yields an average baseline performance of 69.59% on our PEN dataset and produces explanations with quality that is comparable to human output. The contribution of this work is two-fold. (1) EPT-X model: An explainable neural model that sets a baseline for algebraic word problem solving task, in terms of model’s correctness, plausibility, and faithfulness. (2) New dataset: We release a novel dataset PEN (Problems with Explanations for Numbers), which expands the existing datasets by attaching explanations to each number/variable.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Math Word Problem Solving | ALG514 | EPT | Accuracy (%) | 73.91 | #6 of 10 | Archive leaderboard | report |
| Math Word Problem Solving | ALG514 | EPT-X | Accuracy (%) | 67.07 | #9 of 10 | Archive leaderboard | report |
| Math Word Problem Solving | DRAW-1K | EPT | Accuracy (%) | 63.5 | #1 of 5 | Archive leaderboard | report |
| Math Word Problem Solving | DRAW-1K | EPT-X | Accuracy (%) | 56 | #5 of 5 | Archive leaderboard | report |
| Math Word Problem Solving | MAWPS | EPT | Accuracy (%) | 88.7 | #9 of 25 | Archive leaderboard | report |
| Math Word Problem Solving | MAWPS | EPT-X | Accuracy (%) | 84.57 | #13 of 25 | Archive leaderboard | report |
| Math Word Problem Solving | PEN | EPT-X | Accuracy (%) | 69.59 | #1 of 1 | 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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