Could you give me a hint? Generating inference graphs for defeasible reasoning

TL;DR

This paper introduces a method to automatically generate inference graphs for defeasible reasoning using transfer learning, significantly aiding human reasoning and opening new research directions.

Contribution

It presents a novel transfer learning approach to automatically generate inference graphs for defeasible reasoning, reducing manual effort and improving reasoning accuracy.

Findings

Generated graphs are meaningful and useful for reasoning tasks.

Human accuracy improves by 20% when using the generated graphs.

A large dataset of 230,000 influence graphs is provided.

Abstract

Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. A commonly used method in cognitive science and logic 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.)