  Natural language inference (NLI), also known as Recognizing Textual
Entailment (RTE), is an important aspect of natural language understanding.
Most research now uses machine learning and deep learning to perform this task
on specific datasets, meaning their solution is not explainable nor explicit.
To address the need for an explainable approach to RTE, we propose a novel
pipeline that is based on translating text into an Abstract Meaning
Representation (AMR) graph. For this we use a pre-trained AMR parser. We then
translate the AMR graph into propositional logic and use a SAT solver for
automated reasoning. In text, often commonsense suggests that an entailment (or
contradiction) relationship holds between a premise and a claim, but because
different wordings are used, this is not identified from their logical
representations. To address this, we introduce relaxation methods to allow
replacement or forgetting of some propositions. Our experimental results show
this pipeline performs well on four RTE datasets.
