Modeling Logical Content and Pattern Information for Contextual Reasoning

Peiqi Guo, Ping Jian, Xuewen Shi

Abstract: Logical reasoning tasks have recently become a research hotspot in machine reading comprehension communities. This task requires models to answer the question by extracting and utilizing the implicit logical information hidden in the text. Logical information includes logical content information and logical pattern information. When the context and options are relevant in content aspect, such as Necessary Assumption, logical content information can help model to perform better. When it comes to content unrelated situation, such as Logic Principle questions, models need to further distill logical pattern information. Previous works has proposed some strategies, focusing on modeling the logical content information, but there are still some limitations such as long-distance dependency, heavy reliance on external data and internal data augmentation. In addition, the logical pattern information has not received much attention in the previous works, which will cause negative impact on the generalization in practical scenarios. In this paper, we try to improve both effectiveness and generalization of the model. We proposed a novel logical Transformer Capsule Network (LTCN). In this model, to better capture the logical content information, we combine logic graph with Transformer by using biaffine mechanism. And we fill the gap of ignoring logical pattern information by introducing a Capsule Network. Experimental results shows that our model outperforms on both ReClor and LogiQA datasets. Specially, our model has a significant performance improvements on handling more challenging logical reasoning questions.