  Word embeddings have advanced the state of the art in NLP across numerous
tasks. Understanding the contents of dense neural representations is of utmost
interest to the computational semantics community. We propose to focus on
relating these opaque word vectors with human-readable definitions, as found in
dictionaries. This problem naturally divides into two subtasks: converting
definitions into embeddings, and converting embeddings into definitions. This
task was conducted in a multilingual setting, using comparable sets of
embeddings trained homogeneously.
