Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. A commonly used method in philosophy and AI literature is to handcraft argumentation supporting inference graphs. While humans find inference graphs very useful for reasoning, constructing them at scale is difficult. In this paper, we automatically generate such inference graphs through transfer learning from another NLP task that shares the kind of reasoning that inference graphs support. Through automated metrics and human evaluation, we find that our method generates meaningful graphs for the defeasible inference task. Human accuracy on this task improves by 20% by consulting the generated graphs. Our findings open up exciting new research avenues for cases where machine reasoning can help human reasoning. (A dataset of 230,000 influence graphs for each defeasible query is located at: https://tinyurl.com/defeasiblegraphs.)
翻译:诽谤性推理是一种推理模式,可以参照新的证据推翻结论。哲学和AI文献中常用的一种方法是支持推理图的手工艺论证。虽然人类发现推理图对推理非常有用,但很难在规模上构建。在本文中,我们通过从另一个国家实验室项目任务中转学来自动生成这种推理图,后者分享推理图所支持的推理。我们通过自动化计量和人类评估发现,我们的方法为推理工作生成了有意义的图表。通过查阅生成的图表,人类对这项工作的准确性提高了20%。我们的调查结果为机器推理能帮助人类推理的案例开辟了令人振奋的新研究途径。 (关于每个解算论查询的230,000个影响图的数据集在https://tinyurl.com/defeasiblegraphs上。)