Operation-Augmented Numerical Reasoning for Question Answering

Question answering requiring numerical reasoning, which generally involves symbolic operations such as sorting, counting, and addition, is a challenging task. To address such a problem, existing mixture-of-experts (MoE)-based methods design several specific answer predictors to handle different types of questions and achieve promising performance. However, they ignore the modeling and exploitation of fine-grained reasoning-related operations to support numerical reasoning, encountering the inadequacy in reasoning capability and interpretability. To alleviate this issue, we propose OPERA, an operation-augmented numerical reasoning framework. Concretely, we systematically define a scalable operation set to model numerical reasoning. We first identify reasoning-related operations based on context and then softly execute them to imitate the answer reasoning procedure via an operation-aware cross-attention mechanism. Finally, we utilize the operation-augmented semantic representation of execution results to support answer prediction. We verify the effectiveness and generalization of OPERA in two scenarios with different knowledge sources and reasoning capabilities. Specifically, we conduct extensive experiments on two textual datasets, DROP and RACENum, and a table-text hybrid dataset TAT-QA. Experiment results show that OPERA outperforms previous strong methods on the DROP, RACENum, and TAT-QA datasets. Further, we statistically and visually analyze its interpretability.