Abstract
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.- Anthology ID:
- 2022.acl-long.305
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4442–4458
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.305
- DOI:
- 10.18653/v1/2022.acl-long.305
- Cite (ACL):
- Bugeun Kim, Kyung Seo Ki, Sangkyu Rhim, and Gahgene Gweon. 2022. EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4442–4458, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers (Kim et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-long.305.pdf
- Code
- snucclab/ept-x
- Data
- PEN, ALG514, DRAW-1K, MAWPS