Transformers have recently been shown to be capable of reliably performing
logical reasoning over facts and rules expressed in natural language, but
abductive reasoning - inference to the best explanation of an unexpected
observation - has been underexplored despite significant applications to
scientific discovery, common-sense reasoning, and model interpretability.
We present AbductionRules, a group of natural language datasets designed to
train and test generalisable abduction over natural-language knowledge bases.
We use these datasets to finetune pretrained Transformers and discuss their
performance, finding that our models learned generalisable abductive techniques
but also learned to exploit the structure of our data. Finally, we discuss the
viability of this approach to abductive reasoning and ways in which it may be
improved in future work.

该研究旨在探讨 Transformer 网络在自然语言的事实和规则上的逻辑推理，但未充分研究其在归纳推理中的应用。AbductionRules 是一组自然语言数据集，旨在训练和测试自然语言知识库上的归纳推理的可推广性。通过 Fine-tuned 预训练过的 Transformers 模型，研究者发现模型学习了可推广的归纳推理技巧，并利用了数据结构。最后，研究者还探讨了这种方法的可行性以及未来的改进方向。