Papers with non-retrieval-based model

1 papers
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems (N19-1)

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Challenge: Existing methods to solve math word problems require accurate natural language understanding to bridge texts and math expressions.
Approach: They propose a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts.
Outcome: The proposed model outperforms state-of-the-art models and the best non-retrieval-based models over 10% accuracy in a Math23K dataset.

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