Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, we take a step forward in complex reasoning by systematically studying the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests. The experimental results demonstrate that our system endows itself a certain complex reasoning ability, especially the fundamental reading comprehension and challenging logical reasoning capacities. Further analysis also shows the effectiveness of combining the pre-trained models with the task-specific reasoning module, and integrating symbolic knowledge into discrete interpretable reasoning steps in complex reasoning. We further shed a light on the potential future directions, like unsupervised symbolic knowledge extraction, model interpretability, few-shot learning and comprehensive benchmark for complex reasoning.
翻译:复杂的推理旨在根据复杂的规则得出正确的推理。作为人类智慧的标志,它涉及一定程度的明确的解读理解、对逻辑知识的解释和复杂的规则应用。在本文件中,我们通过系统地研究法学院招生测试(LSAT)的三项具有挑战性和一般领域性的任务,包括分析推理、逻辑推理和阅读理解,在复杂规则应用方面迈出了一步。我们提出了一个混合推理系统,以综合这三项任务,并在LSAT测试中取得令人印象深刻的总体业绩。实验结果表明,我们的系统拥有某种复杂的推理能力,特别是基本的阅读理解和质疑逻辑推理能力。进一步的分析还表明,将预先培训的模式与具体任务推理模块相结合,并将象征性知识纳入复杂推理中可独立解释的推理步骤的有效性。我们进一步阐明未来的潜在方向,如不受监督的象征性知识提取、模型解释性、少见的学习和复杂推理的全面基准。