Neural Natural Language Commonsense Reasoning via Clause Patterns

Abstract

Human reasoning is not only based on logical implication relations, but also on common sense inference based on everyday knowledge. In this study, we consider knowledge in this case as natural language sentences themselves, which can express complex contents, and use Clause Patterns to learn natural language inferences in which both premises and consequences are sentences directly from a corpus as a deep learning model. We pre-processed 1.57 million premise/consequence pair sentences extracted from the corpus, trained on those pair sentences for which the inference is plausible using T5, and constructed a non-logical linguistic inference by natural language sentence generation. On the test data, we manually evaluated the inferences generated from the premises, and found that 65.8% of the inferences were valid. We further discuss the reasons why valid inferences were not obtained and discuss possible improvements and future possibilities.