ArMATH++: Enhancing Arabic mathematical word problem solvers through data augmentation and synthetic data generation

Math word problem (MWP) is a famous type of math problems that require natural language understanding along with mathematical reasoning. MWP solvers should understand the problem’s context, identify relevant quantities, infer relationships and finally deduce the solving equation. In order to develop such solvers, suitable MWP dataset should be constructed to be utilized in training these solvers. Despite the presence of MWP datasets in different languages, few reliable Arabic MWP datasets were publicly found. Addressing this gap, we studied the most recent data augmentation strategies and their adaptations to the Arabic language and applied them to the most promising Arabic MWPs “ArMATH” extending its vocabulary and generalization capabilities. Our experiments demonstrated that augmenting the training data helps the models better solve MWPs, increasing the equation and answer accuracies by 5.2 and 5.1 scoring 73 and 74.2 respectively. ArMATH + + , reliable Arabic MWPs dataset was introduced. With a size of 33,918 samples, ArMATH + + can be utilized in training deep neural networks and LLMs finetuning.