This thesis studies to automatically solve Arabic Math Word Problems (MWPs) by deep learning models. MWP is a text description of a mathematical problem, which should be solved by deriving a math equation and reach the answer. Due to their strong learning capacity, deep learning based models can learn from the given problem description and generate the correct math equation for solving the problem. Effective models have been developed for solving MWPs in English and Chinese. However, Arabic MWPs are rarely studied. To initiate the study in Arabic MWPs, this thesis contributes the first large-scale dataset for Arabic MWPs, which contain 6,000 samples. Each sample is composed of an Arabic MWP description and the corresponding equation to solve this MWP. Arabic MWP solvers are then built with deep learning models, and verified on this dataset for their effectiveness. In addition, a transfer learning model is built to let the high-resource Chinese MWP solver to promote the performance of the low-resource Arabic MWP solver. This work is the first to use deep learning methods to solve Arabic MWP and the first to use transfer learning to solve MWP across different languages. The solver enhanced by transfer learning has accuracy 74.15%, which is 3% higher than the baseline that does not use transfer learning. In addition, the accuracy is more than 7% higher than the baseline for templates with few samples representing them. Furthermore, The model can generate new sequences that were not seen before during the training with an accuracy of 27% (11% higher than the baseline).
|Date made available
|KAUST Research Repository