Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 2021
DOI: 10.18653/v1/2021.findings-acl.456
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Could you give me a hint ? Generating inference graphs for defeasible reasoning

Abstract: Defeasible reasoning is a 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 a related NLP task that shares the kind of reasoning that inference graphs su… Show more

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Cited by 5 publications
(14 citation statements)
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“…By interspersing the inference rules, the storyline generations should create a coherent story that follows logical connections and causal relationships between events. Madaan et al (2021a) employ T5 to generate inference graphs for defeasible inference (Rudinger et al, 2020). In this mode of reasoning, given a premise, a hypothesis may be weakened or overturned in light of new evidence.…”
Section: Inference Rule Generationmentioning
confidence: 99%
“…By interspersing the inference rules, the storyline generations should create a coherent story that follows logical connections and causal relationships between events. Madaan et al (2021a) employ T5 to generate inference graphs for defeasible inference (Rudinger et al, 2020). In this mode of reasoning, given a premise, a hypothesis may be weakened or overturned in light of new evidence.…”
Section: Inference Rule Generationmentioning
confidence: 99%
“…Inspired by past results (Madaan et al, 2021) that humans found inference graphs useful for defeasible inference, we investigate whether neural models can benefit from envisioning the question scenario using an inference graph before answering a defeasible inference query.…”
Section: Approachmentioning
confidence: 99%
“…Inference graphs were introduced in philosophy by Pollock (2009) to aid defeasible reasoning for humans, and in NLP by Tandon et al (2019) for a counterfactual reasoning task. We interpret the inference graphs as having four kinds of nodes (Pollock, 2009;Madaan et al, 2021):…”
Section: Approachmentioning
confidence: 99%
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