Papers › Learning Prototypical Functions for Physical Artifacts
Learning Prototypical Functions for Physical Artifacts
Tianyu Jiang, Ellen Riloff
Humans create things for a reason. Ancient people created spears for hunting, knives for cutting meat, pots for preparing food, etc. The prototypical function of a physical artifact is a kind of commonsense knowledge that we rely on to understand natural language. For example, if someone says {``}She borrowed the book{''} then you would assume that she intends to read the book, or if someone asks {``}Can I use your knife?{''} then you would assume that they need to cut something. In this paper, we introduce a new NLP task of learning the prototypical uses for human-made physical objects. We use frames from FrameNet to represent a set of common functions for objects, and describe a manually annotated data set of physical objects labeled with their prototypical function. We also present experimental results for this task, including BERT-based models that use predictions from masked patterns as well as artifact sense definitions from WordNet and frame definitions from FrameNet.
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