Could you give me a hint? Generating inference graphs for defeasible reasoning
This addresses the challenge of scaling inference graph construction for defeasible reasoning in cognitive science and logic, though it appears incremental as it builds on transfer learning from existing NLP tasks.
The paper tackles the problem of automatically generating inference graphs for defeasible reasoning, which is difficult to construct at scale, and finds that their method generates meaningful graphs, improving human accuracy on the task by 20%.
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.)