Unified Semantic Typing with Meaningful Label Inference. (arXiv:2205.01826v1 [cs.CL])

Semantic typing aims at classifying tokens or spans of interest in a textual
context into semantic categories such as relations, entity types, and event
types. The inferred labels of semantic categories meaningfully interpret how
machines understand components of text. In this paper, we present UniST, a
unified framework for semantic typing that captures label semantics by
projecting both inputs and labels into a joint semantic embedding space. To
formulate different lexical and relational semantic typing tasks as a unified
task, …

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