2021/05/12 by Aman Madaan, Dheeraj Rajagopal, Madaan, Aman +7 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Semantic Web and Ontologies #Software Engineering Research #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2105.05418
openalex publication_date 2021/05/12 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Defeasible reasoning is the mode of reasoning where conclusions can be\noverturned by taking into account new evidence. A commonly used method in\ncognitive science and logic literature is to handcraft argumentation supporting\ninference graphs. While humans find inference graphs very useful for reasoning,\nconstructing them at scale is difficult. In this paper, we automatically\ngenerate such inference graphs through transfer learning from another NLP task\nthat shares the kind of reasoning that inference graphs support. Through\nautomated metrics and human evaluation, we find that our method generates\nmeaningful graphs for the defeasible inference task. Human accuracy on this\ntask improves by 20% by consulting the generated graphs. Our findings open up\nexciting new research avenues for cases where machine reasoning can help human\nreasoning. (A dataset of 230,000 influence graphs for each defeasible query is\nlocated at: https://tinyurl.com/defeasiblegraphs.)\n