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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.266.pdf
- Code
- debymf/nl-ps