Rule Learning as Machine Translation using the Atomic Knowledge Bank
Kristoffer Æsøy Ana Ozaki
Machine learning models, and in particular language models, are being applied
to various tasks that require reasoning. While such models are good at
capturing patterns their ability to reason in a trustable and controlled manner
is frequently questioned. On the other hand, logic-based rule systems allow for
controlled inspection and already established verification methods. However it
is well-known that creating such systems manually is time-consuming and prone
to errors. We explore the capability of transformers to translate sentences
expressing rules in natural language into logical rules. We see reasoners as
the most reliable tools for performing logical reasoning and focus on
translating language into the format expected by such tools. We perform
experiments using the DKET dataset from the literature and create a dataset for
language to logic translation based on the Atomic knowledge bank.