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。

具有不完整信息的自然语言推断

Natural Language Deduction with Incomplete Information

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.

提出了一个处理模糊的假设的新系统，使用自然语言生成模型来从一个前提中推出另一个前提并以双向的方式搜索。 使用回路验证程序过滤质量不佳的生成来确保正确性。 该系统在修改后的 EntailmentBank 数据集和名为 “Everyday Norms：Why Not?“的新数据集上表现出可以恢复跨领域和非领域的前提的能力。

自然语言推断与不完整信息

An interpretable system for open-domain reasoning needs to express its
reasoning process in a transparent form. Natural language is an attractive
representation for this purpose -- it is both highly expressive and easy for
humans to understand. However, manipulating natural language statements in
logically consistent ways is hard: models must cope with variation in how
meaning is expressed while remaining precise. In this paper, we describe
ParaPattern, a method for building models to generate deductive inferences from
diverse natural language inputs without direct human supervision. We train
BART-based models (Lewis et al., 2020) to generate the result of applying a
particular logical operation to one or more premise statements. Crucially, we
develop a largely automated pipeline for constructing suitable training
examples from Wikipedia. We evaluate our models using out-of-domain sentence
compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et
al., 2021) datasets as well as targeted perturbation sets. Our results show
that our models are substantially more accurate and flexible than baseline
systems. ParaPattern achieves 85% validity on examples of the 'substitution'
operation from EntailmentBank without the use of any in-domain training data,
matching the performance of a model fine-tuned for EntailmentBank. The full
source code for our method is publicly available.

本研究提出了 ParaPattern，该方法利用自动化流程从维基百科生成训练数据，并使用 BART 模型在无直接人类监督下从多样的自然语言输入中生成演绎推理，结果表明，ParaPattern 模型在无领域训练数据的情况下，表现优于基线系统。