Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem
Wenqi Zhang, Yongliang Shen, Yanna Ma, Xiaoxia Cheng, Zeqi Tan, Qingpeng Nong, Weiming Lü · 2022
Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation.Current sequence-to-tree or relation extraction methods regard this only from a fixed view, struggling to simultaneously handle complex semantics and diverse equations.However, human solving naturally involves two consistent reasoning views: top-down and bottom-up, just as math equations also can be expressed in multiple equivalent forms: pre-order and postorder.We propose a multi-view consistent contrastive learning for a more complete semanticsto-equation mapping.The entire process is decoupled into two independent but consistent views: top-down decomposition and bottomup construction, and the two reasoning views are aligned in multi-granularity for consistency, enhancing global generation and precise reasoning.Experiments on multiple datasets across two languages show our approach significantly outperforms the existing baselines, especially on complex problems 1 .We also show after consistent alignment, multi-view can absorb the merits of both views and generate more diverse results consistent with the mathematical laws.