Mathematical Language

Mathematical language processing aims to develop computational methods for understanding and generating mathematical text, a field currently lagging behind natural language processing. Research focuses on creating annotated corpora and benchmarks to evaluate models, often adapting existing neural architectures like GPT-2 or employing modular deep learning approaches such as language arithmetic to improve cross-lingual capabilities and address the limitations of existing multilingual models. These advancements are crucial for improving learning tools, automating mathematical tasks, and facilitating cross-disciplinary research by bridging the gap between human-readable text and formal mathematical expressions.