Ongraph Vector Computing for Solving Explicit Arithmetic Word Problems

Automatic solutions to Arithmetic Word Problems (AWPs) continue to attract significant attention due to their potential uses in fact-checking, validation as well as problem-solving across science, industry, and education. For the latter field specifically, there is significant untapped potential from the automation of AWPs for the full integration of intelligent tutors into educational processes, being the focus of this work. To aid the demystification of AWP, a range of algorithms has been proposed in recent years with some of these documenting incremental increases in accuracy. Some, however, are not suitable for broad application as they cannot generate readable solutions required for intelligent tutoring. This work aims to increase the accuracy of explicit AWP solutions, bring transparency to the AWP problem-solving process, and generate intermediate readable solutions by proposing a hybrid algorithm that combines superior features from existing methods with “Ongraph” Vector Computing. The essential creativity lies in that the proposed Ongraph vector computing fuses the bests of graph and vector computing and that it integrates the vector syntax-semantics method. Experimental results obtained from the test on six authoritative datasets confirm that the proposed algorithm improves accuracy for automated solutions of AWPs thus making a significant contribution to the body of work on AWP-based problem-solving.