Computer Science > Computation and Language
[Submitted on 31 Oct 2018 (v1), last revised 7 Feb 2019 (this version, v3)]
Title:ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
View PDFAbstract:We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.
Submission history
From: Maarten Sap [view email][v1] Wed, 31 Oct 2018 22:57:51 UTC (537 KB)
[v2] Thu, 15 Nov 2018 20:12:03 UTC (558 KB)
[v3] Thu, 7 Feb 2019 19:52:21 UTC (553 KB)
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