We propose using automatically generated natural language definitions of
contextualised word usages as interpretable word and word sense
representations. Given a collection of usage examples for a target word, and
the corresponding data-driven usage clusters (i.e., word senses), a definition
is generated for each usage with a specialised Flan-T5 language model, and the
most prototypical definition in a usage cluster is chosen as the sense label.
We demonstrate how the resulting sense labels can make existing approaches to
semantic change analysis more interpretable, and how they can allow users --
historical linguists, lexicographers, or social scientists -- to explore and
intuitively explain diachronic trajectories of word meaning. Semantic change
analysis is only one of many possible applications of the `definitions as
representations' paradigm. Beyond being human-readable, contextualised
definitions also outperform token or usage sentence embeddings in
word-in-context semantic similarity judgements, making them a new promising
type of lexical representation for NLP.Comment: ACL 202