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We introduce a new representation language to model operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.

Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions.

We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.

Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions.

Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.

MACHINE TRANSLATION MATH WORD PROBLEM SOLVING SEMANTIC PARSING

Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.

To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.

This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.

This paper presents a deep neural solver to automatically solve math word problems.

FEATURE ENGINEERING MACHINE TRANSLATION MATH WORD PROBLEM SOLVING SEMANTIC PARSING