  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, we incorporate task descriptions to be jointly encoded with the input,
allowing UniST to be adapted to different tasks without introducing
task-specific model components. UniST optimizes a margin ranking loss such that
the semantic relatedness of the input and labels is reflected from their
embedding similarity. Our experiments demonstrate that UniST achieves strong
performance across three semantic typing tasks: entity typing, relation
classification and event typing. Meanwhile, UniST effectively transfers
semantic knowledge of labels and substantially improves generalizability on
inferring rarely seen and unseen types. In addition, multiple semantic typing
tasks can be jointly trained within the unified framework, leading to a single
compact multi-tasking model that performs comparably to dedicated single-task
models, while offering even better transferability.
