Natural Language Deduction through Search over Statement Compositions

In settings from fact-checking to question answering, we frequently want to know whether a collection of evidence entails a hypothesis. Existing methods primarily focus on end-to-end discriminative versions of this task, but less work has treated the generative version in which a model searches over the space of entailed statements to derive the hypothesis. We propose a system for natural language deduction that decomposes the task into separate steps coordinated by best-first search, producing a tree of intermediate conclusions that faithfully reflects the system's reasoning process. Our experiments demonstrate that the proposed system can better distinguish verifiable hypotheses from unverifiable ones and produce natural language explanations that are more internally consistent than those produced by an end-to-end T5 model.