In recent years, there has been remarkable progress in leveraging Language Models (LMs),
encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models …

This paper presents a deep neural solver to automatically solve math word problems. In
contrast to previous statistical learning approaches, we directly translate math word …

Most existing neural models for math word problems exploit Seq2Seq model to generate
solution expressions sequentially from left to right, whose results are far from satisfactory …

Recent work across several AI subdisciplines has focused on automatically solving math
word problems. In this paper we introduce MAWPS, an online repository of Math Word …

Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic
math word problem solving. Despite its simplicity, a drawback still remains: a math word …

This paper formalizes the problem of solving multi-sentence algebraic word problems as that
of generating and scoring equation trees. We use integer linear programming to generate …

The design of automatic solvers to arithmetic math word problems has attracted
considerable attention in recent years and a large number of datasets and methods have …

Solving mathematical word problems (MWPs) automatically is challenging, primarily due to
the semantic gap between human-readable words and machine-understandable logics …

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …

Automatic math problem solving has recently attracted increasing attention as a long-
standing AI benchmark. In this paper, we focus on solving geometric problems, which …