Building a Commonsense Inference Dataset based on Basic Events and its Application

Abstract

We propose a method for building a commonsense inference dataset based on basic events. Specifically, we automatically extract contingent pairs of basic event expressions such as “I'm hungry, so I have a meal” from text, verify by crowdsourcing, and automatically generate commonsense inference problems regarding the contingent relation between basic events. We built a commonsense inference dataset of 100k problems by the proposed method and conducted experiments to investigate the model performance. The results showed that there is a performance gap between high-performance language models and humans. In addition, we automatically generated large-scale pseudo problems by utilizing the scalability of the proposed method and investigated the effects by the data augmentation on the commonsense inference task and the related tasks. The results demonstrated the effectiveness of learning extensive contingent knowledge for both the commonsense inference task and the related tasks, which suggests the importance of contingent reasoning.