Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text
arXiv:2004.14959
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.
12th Language Resources and Evaluation Conference (LREC), Marseille, France, 2020 (Language Resource Paper)
References in corpus (6)
- Premise Selection for Mathematics by Corpus Analysis and Kernel Methods
- DeepMath - Deep Sequence Models for Premise Selection
- Premise Selection and External Provers for HOL4
- Preliminary Exploration of Formula Embedding for Mathematical Information Retrieval: can mathematical formulae be embedded like a natural language?
- First Experiments with Neural Translation of Informal to Formal Mathematics
- Equation Embeddings