Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this nature, semantic parsing-based methods predict interpretable but complex logical forms. However, logical form generation is nontrivial and even a little perturbation in a logical form will lead to wrong answers. To alleviate this issue, multi-predictor -based methods are proposed to directly predict different types of answers and achieve improvements. However, they ignore the utilization of symbolic operations and encounter a lack of reasoning ability and interpretability. To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability. Specifically, operations are first selected and then softly executed to simulate the answer reasoning procedure. Extensive experiments on both DROP and RACENum datasets show the reasoning ability of OPERA. Moreover, further analysis verifies its interpretability.
SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Merchants Union Consumer Finance Company Limited（招商局联融消费金融股份有限公司）
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning
University of Waterloo（多伦多大学）；Hong Kong University of Science and Technology（香港科技大学）；University of Science and Technology of China（中国科学技术大学）；Vector Institute（向量研究所）
