Improving Math Word Problems Solver with Logical Semantic Similarity

Math word problems (MWPs) solving has achieved promising results recently. However, most existing methods focus only on learning the mapping function between problem text and the target equation, ignoring the logical semantic similarity among problem texts with same target prototype equation and different topic description. Under the condition of maintaining the logical semantics, modifying only the topic words of a question text will let these models generate completely different equations and answers. In this paper, we propose a novel approach called Logical Semantic Aggregator (LSA) which solves the math word problem efficiently and effectively by extracting logical semantics from problem texts for giving a guidance to equation generation. In addition, a Implicit Constants Predictor (ICP) mechanism is used to predict the corresponding numerical label, which is used as the information prompts to improve the semantic representations of MWPs. Experimental results on the Math23K dataset revealed that our proposed methods can achieve better performance than baselines and higher equation accuracy with the help of implicit numerical label prompts.