Math Word Problem Solving with Explicit Numerical Values
In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.
Code (1)
Tasks
MathMath Word Problem SolvingSimilar Papers 제목 키워드 기반
Does ChatGPT Comprehend the Place Value in Numbers When Solving Math Word Problems?
With the recent advancements in GPT, there’s been a growing trend to integrate GPT in solving math word problems using strategies such as “Chain-of-Thought”(CoT) and “Program-of-Thought”(PoT). Based on the observation th…
MathMath Word Problem SolvingMWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solv…
Common Sense ReasoningLanguage ModelingLanguage ModellingMath+3MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solv…
Language ModelingLanguage ModellingMathMath Word Problem Solving+2DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert do…
MathEnhancing the Transformer with Explicit Relational Encoding for Math Problem Solving
We incorporate Tensor-Product Representations within the Transformer in order to better support the explicit representation of relation structure. Our Tensor-Product Transformer (TP-Transformer) sets a new state of the a…
MathQuestion Answering