A Goal-Driven Tree-Structured Neural Model for Math Word Problems

Most existing neural models for math word problems exploit Seq2Seq model to generate solutionexpressions sequentially from left to right, whoseresults are far from satisfactory due to the lackof goal-driven mechanism commonly seen in human problem solving. This paper proposes a treestructured neural model to generate expression treein a goal-driven manner. Given a math word problem, the model first identifies and encodes its goalto achieve, and then the goal gets decomposed intosub-goals combined by an operator in a top-downrecursive way. The whole process is repeated until the goal is simple enough to be realized by aknown quantity as leaf node. During the process,two-layer gated-feedforward networks are designedto implement each step of goal decomposition, anda recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than thesimple goals of subtrees. Experimental results onthe dataset Math23K have shown that our treestructured model outperforms significantly severalstate-of-the-art models.