Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text

Deborah Ferreira, André Freitas


Abstract
Mathematical text is written using a combination of words and mathematical expressions. This combination, along with a specific way of structuring sentences makes it challenging for state-of-art NLP tools to understand and reason on top of mathematical discourse. In this work, we propose a new NLP task, the natural premise selection, which is used to retrieve supporting definitions and supporting propositions that are useful for generating an informal mathematical proof for a particular statement. We also make available a dataset, NL-PS, which can be used to evaluate different approaches for the natural premise selection task. Using different baselines, we demonstrate the underlying interpretation challenges associated with the task.
Anthology ID:
2020.lrec-1.266
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2175–2182
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.266
DOI:
Bibkey:
Cite (ACL):
Deborah Ferreira and André Freitas. 2020. Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2175–2182, Marseille, France. European Language Resources Association.
Cite (Informal):
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text (Ferreira & Freitas, LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.266.pdf
Code
 debymf/nl-ps