EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers
Bugeun Kim, Kyung Seo Ki, Sangkyu Rhim, Gahgene Gweon · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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.