MathNLP 2025 : MathNLP 2025: The 3rd Workshop on Mathematical Natural Language Processing
We welcome contributions of previously unpublished papers, which could be either long (8 pages) or short (4 pages). All submissions will be peer-reviewed by multiple reviewers. The authors' identities must be concealed to enable a double-blind peer review.
MathNLP welcomes both archival and non-archival submissions. Only archival submissions will be included in the proceedings.
We are particularly interested in (but not limited to) works related to the following topics:
Neural/Neuro-symbolic architectures to support mathematical natural language inference;
Large Language Models for Mathematics;
Equational embeddings;
Autoformalisation and translation from natural language to formal languages (and vice-versa);
Linguistic analysis of mathematical discourse and argumentation relations in the context of mathematical text;
- Probing mathematical understanding of state-of-the-art models;
- Adaptation of NLP tasks for mathematical discourse;
- NLP applied to mathematics education;
- Premise selection over mathematical text;
- Understanding and typing of variables in mathematical text;
- Retrieval of equations/formulas/expressions based on textual queries;
- Retrieval of textual context based on equational queries.
MathNLP welcomes both archival and non-archival submissions. Only archival submissions will be included in the proceedings.
We are particularly interested in (but not limited to) works related to the following topics:
Neural/Neuro-symbolic architectures to support mathematical natural language inference;
Large Language Models for Mathematics;
Equational embeddings;
Autoformalisation and translation from natural language to formal languages (and vice-versa);
Linguistic analysis of mathematical discourse and argumentation relations in the context of mathematical text;
- Probing mathematical understanding of state-of-the-art models;
- Adaptation of NLP tasks for mathematical discourse;
- NLP applied to mathematics education;
- Premise selection over mathematical text;
- Understanding and typing of variables in mathematical text;
- Retrieval of equations/formulas/expressions based on textual queries;
- Retrieval of textual context based on equational queries.