  Beginning with McCarthy's Advice Taker (1959), AI has pursued the goal of
providing a system with explicit, general knowledge and having the system
reason over that knowledge. However, expressing the knowledge in a formal
(logical or probabilistic) representation has been a major obstacle to this
research. This paper investigates a modern approach to this problem where the
facts and rules are provided as natural language sentences, thus bypassing a
formal representation. We train transformers to reason (or emulate reasoning)
over these sentences using synthetically generated data. Our models, that we
call RuleTakers, provide the first empirical demonstration that this kind of
soft reasoning over language is learnable, can achieve high (99%) accuracy, and
generalizes to test data requiring substantially deeper chaining than seen
during training (95%+ scores). We also demonstrate that the models transfer
well to two hand-authored rulebases, and to rulebases paraphrased into more
natural language. These findings are significant as it suggests a new role for
transformers, namely as limited "soft theorem provers" operating over explicit
theories in language. This in turn suggests new possibilities for
explainability, correctability, and counterfactual reasoning in
question-answering.
