MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
This work addresses the numerical reasoning bottleneck in math word problem solving, offering a domain-specific advancement for educational AI and natural language processing.
The paper tackles the challenge of number representation in math word problem solving by proposing MWP-BERT, a pre-trained language model that injects numerical properties into symbolic placeholders, achieving improved performance on English and Chinese benchmarks.
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 solving usually replace real numbers with symbolic placeholders to focus on logic reasoning. However, different from common symbolic reasoning tasks like program synthesis and knowledge graph reasoning, MWP solving has extra requirements in numerical reasoning. In other words, instead of the number value itself, it is the reusable numerical property that matters more in numerical reasoning. Therefore, we argue that injecting numerical properties into symbolic placeholders with contextualized representation learning schema can provide a way out of the dilemma in the number representation issue here. In this work, we introduce this idea to the popular pre-training language model (PLM) techniques and build MWP-BERT, an effective contextual number representation PLM. We demonstrate the effectiveness of our MWP-BERT on MWP solving and several MWP-specific understanding tasks on both English and Chinese benchmarks.