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Math word problem (MWP) is a challenging and critical task in naturallanguage processing. Many recent studies formalize MWP as a generation task andhave adopted sequence-to-sequence models to transform problem descriptions tomathematical expressions. However, mathematical expressions are prone to minormistakes while the generation objective does not explicitly handle suchmistakes. To address this limitation, we devise a new ranking task for MWP andpropose Generate & Rank, a multi-task framework based on a generativepre-trained language model. By joint training with generation and ranking, themodel learns from its own mistakes and is able to distinguish between correctand incorrect expressions. Meanwhile, we perform tree-based disturbancespecially designed for MWP and an online update to boost the ranker. Wedemonstrate the effectiveness of our proposed method on the benchmark and theresults show that our method consistently outperforms baselines in alldatasets. Particularly, in the classical Math23k, our method is 7% (78.4%$\rightarrow$ 85.4%) higher than the state-of-the-art.

Generate & Rank: A Multi-task Framework for Math Word Problems