论文标题
通过搜索陈述作品来扣除自然语言
Natural Language Deduction through Search over Statement Compositions
论文作者
论文摘要
在从事实检查到问题回答的设置中，我们经常想知道证据集合（前提）是否需要假设。现有方法主要集中于此任务的端到端判别版本，但是较少的工作对待了生成版本，在该版本中，模型在该处所需的陈述空间中进行搜索以建设性地提出假设。我们提出了一个系统，通过将任务分解为通过搜索过程协调的单独步骤来进行自然语言进行这种演绎推理的系统，从而产生了一个中间结论的树，忠实地反映了系统的推理过程。我们在IntailmentBank数据集（Dalvi等，2021）上进行的实验表明，所提出的系统可以在拒绝错误的系统时成功证明真实的陈述。此外，它比端到端T5模型产生的自然语言解释具有绝对的步骤有效性17％。
In settings from fact-checking to question answering, we frequently want to know whether a collection of evidence (premises) entails a hypothesis. Existing methods primarily focus on the end-to-end discriminative version of this task, but less work has treated the generative version in which a model searches over the space of statements entailed by the premises to constructively derive the hypothesis. We propose a system for doing this kind of deductive reasoning in natural language by decomposing the task into separate steps coordinated by a search procedure, producing a tree of intermediate conclusions that faithfully reflects the system's reasoning process. Our experiments on the EntailmentBank dataset (Dalvi et al., 2021) demonstrate that the proposed system can successfully prove true statements while rejecting false ones. Moreover, it produces natural language explanations with a 17% absolute higher step validity than those produced by an end-to-end T5 model.
