Commonsense knowledge

Cognitive psychology

Biology

Isolation (microbiology)

Knowledge extraction

Commonsense reasoning

Atlas (anatomy)

Data mining

Computer science

Relation (database)

Cognitive science

Paleontology

Natural language processing

Psychology

Inference

Microbiology

Artificial intelligence

Proceedings of the ... AAAI Conference on Artificial Intelligence

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.

ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning