  We present a dataset of word usage graphs (WUGs), where the existing WUGs for
multiple languages are enriched with cluster labels functioning as sense
definitions. They are generated from scratch by fine-tuned encoder-decoder
language models. The conducted human evaluation has shown that these
definitions match the existing clusters in WUGs better than the definitions
chosen from WordNet by two baseline systems. At the same time, the method is
straightforward to use and easy to extend to new languages. The resulting
enriched datasets can be extremely helpful for moving on to explainable
semantic change modeling.
