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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.
ATHENA: Mathematical Reasoning with Thought Expansion
NLP
Math Word Problem
ML
Handling Ambiguous Rationales in Natural Language Reasoning
Natural Language Reasoning
NLP
ML
Language Proficiency Enhanced Knowledge Tracing
Knowledge Tracing
Student Modeling
Learning Analytics
Language Proficiency
Learning with Limited Data using Compositionality in Language
NLP
ML
교육 현장의 목소리를 반영한 LMS 데이터 기반의 실용적인 학습 성취 조기 예측 방법 제안
LMS
Education
Emerging Knowledge from Pre-Trained Language Models for Psycholinguistic Analysis
Psycholinguistic Analysis
NLP
ML
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