Logical reasoning of text requires understanding critical logical information
in the text and performing inference over them. Large-scale pre-trained models
for logical reasoning mainly focus on word-level semantics of text while
struggling to capture symbolic logic. In this paper, we propose to understand
logical symbols and expressions in the text to arrive at the answer. Based on
such logical information, we not only put forward a context extension framework
but also propose a data augmentation algorithm. The former extends the context
to cover implicit logical expressions following logical equivalence laws. The
latter augments literally similar but logically different instances to better
capture logical information, especially logical negative and conditional
relationships. We conduct experiments on ReClor dataset. The results show that
our method achieves the state-of-the-art performance, and both logic-driven
context extension framework and data augmentation algorithm can help improve
the accuracy. And our multi-model ensemble system is the first to surpass human
performance on both EASY set and HARD set of ReClor.

本研究提出了一种理解文本中逻辑符号和表达式以得出答案的方法，并基于此提出了一种扩展文本上下文的框架和一种数据增广算法，可以更好地捕捉逻辑信息，特别是关于逻辑否定和条件关系，结果表明这种方法在 ReClor 数据集上实现了最先进的性能，多模型集成系统是第一个在 ReClor 的 EASY 和 HARD 数据集上超过人类表现的系统。