Research list

Math Word Problem (MWP) solving involves understanding mathematical questions expressed in natural language and deriving the appropriate mathematical equations. Traditional approaches heavily rely on simple lexical pattern matching, limiting their flexibility in diverse real-world scenarios. Our co-authored paper introduces ATHENA (Attention-based THought Expansion Network Architecture), designed to mimic human cognitive processes for more generalized and robust mathematical reasoning.
Let's explore how ATHENA achieves robust performance and why this matters for mathematical reasoning in AI.
Research Background
  Math word problem (MWP) solving involves translating complex linguistic descriptions into mathematical expressions. Traditional models tend to memorize lexical patterns rather than understand mathematical principles and procedures, limiting their ability to generalize to unseen or slightly varied problems.
Consider the following cases: calculating the area of a rectangle and determining how many items can be evenly distributed across containers. While both require multiplication, they involve different types of conceptual understanding.