  Transformers have been shown to be able to perform deductive reasoning on a
logical rulebase containing rules and statements written in natural language.
Recent works show that such models can also produce the reasoning steps (i.e.,
the proof graph) that emulate the model's logical reasoning process. Currently,
these black-box models generate both the proof graph and intermediate
inferences within the same model and thus may be unfaithful. In this work, we
frame the deductive logical reasoning task by defining three modular
components: rule selection, fact selection, and knowledge composition. The rule
and fact selection steps select the candidate rule and facts to be used and
then the knowledge composition combines them to generate new inferences. This
ensures model faithfulness by assured causal relation from the proof step to
the inference reasoning. To test our framework, we propose FaiRR (Faithful and
Robust Reasoner) where the above three components are independently modeled by
transformers. We observe that FaiRR is robust to novel language perturbations,
and is faster at inference than previous works on existing reasoning datasets.
Additionally, in contrast to black-box generative models, the errors made by
FaiRR are more interpretable due to the modular approach.
