MWP-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 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.

PDF Abstract Findings (NAACL) 2022 PDF Findings (NAACL) 2022 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Math Word Problem Solving Math23K MWP-BERT Accuracy (5-fold) 82.4 # 6
Accuracy (training-test) 84.7 # 6
Math Word Problem Solving MathQA MWP-BERT Answer Accuracy 76.6 # 5


No methods listed for this paper. Add relevant methods here