A growing body of work studies how to answer a question or verify a claim by
generating a natural language "proof": a chain of deductive inferences yielding
the answer based on a set of premises. However, these methods can only make
sound deductions when they follow from evidence that is given. We propose a new
system that can handle the underspecified setting where not all premises are
stated at the outset; that is, additional assumptions need to be materialized
to prove a claim. By using a natural language generation model to abductively
infer a premise given another premise and a conclusion, we can impute missing
pieces of evidence needed for the conclusion to be true. Our system searches
over two fringes in a bidirectional fashion, interleaving deductive
(forward-chaining) and abductive (backward-chaining) generation steps. We
sample multiple possible outputs for each step to achieve coverage of the
search space, at the same time ensuring correctness by filtering low-quality
generations with a round-trip validation procedure. Results on a modified
version of the EntailmentBank dataset and a new dataset called Everyday Norms:
Why Not? show that abductive generation with validation can recover premises
across in- and out-of-domain settings

通过混合演绎（前向）和择优推断（后向）生成步骤进行阴性推断，以验证其他假设的缺失证据，避免致信任错误。