Mapping probability word problems to executable representations

Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans


Abstract
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such word problems. In a two-step approach, the problem text is first mapped to a formal representation in a declarative language using a sequence-to-sequence model, and then the resulting representation is executed using a probabilistic programming system to provide the answer. Our best performing model incorporates general-domain contextualised word representations that were finetuned using transfer learning on another in-domain dataset. We also apply end-to-end models to this task, which bring out the importance of the two-step approach in obtaining correct solutions to probability problems.
Anthology ID:
2021.emnlp-main.294
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3627–3640
Language:
URL:
https://aclanthology.org/2021.emnlp-main.294
DOI:
10.18653/v1/2021.emnlp-main.294
Bibkey:
Cite (ACL):
Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, and Walter Daelemans. 2021. Mapping probability word problems to executable representations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3627–3640, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Mapping probability word problems to executable representations (Suster et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.294.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.294.mp4
Data
MathQA