Premise Selection in Natural Language Mathematical Texts

Deborah Ferreira, André Freitas


Abstract
The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.
Anthology ID:
2020.acl-main.657
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7365–7374
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.657
DOI:
10.18653/v1/2020.acl-main.657
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.657.pdf
Video:
 http://slideslive.com/38929402