Large-scale pre-trained models like BERT, have obtained a great success in various Natural
Language Processing (NLP) tasks, while it is still a challenge to adapt them to the math …

Mathematical formulas are commonly used to demonstrate theories and basic fundamentals
in the Science, Technology, Engineering, and Mathematics (STEM) domain. The burgeoning …

Automating discovery in mathematics and science will require sophisticated methods of
information extraction and abstract reasoning, including models that can convincingly …

We study the problem of generating arithmetic math word problems (MWPs) given a math
equation that specifies the mathematical computation and a context that specifies the …

Abstract The ARQMath Lab at CLEF considers finding answers to new mathematical
questions among posted answers on a community question answering site (Math Stack …

This paper provides an overview of the third and final year of the Answer Retrieval for
Questions on Math (ARQMath-3) lab, run as part of CLEF 2022. ARQMath has aimed to …

With the recent success of dense retrieval methods based on bi-encoders, studies have
applied this approach to various interesting downstream retrieval tasks with good efficiency …

In this paper, we study how to continually pre-train language models for improving the
understanding of math problems. Specifically, we focus on solving a fundamental challenge …

Mathematical language in scientific communications and educational scenarios is important
yet relatively understudied compared to natural languages. Recent works on mathematical …

Currently, the non-transparent computing process of deep learning has become a significant
reason hindering its further development. The Neural-Symbolic (NS) system formed by …