Concept-Best-Matching: Evaluating Compositionality in Emergent
  Communication
Boaz Carmeli Yonatan Belinkov Ron Meir
Artificial agents that learn to communicate in order to accomplish a given
task acquire communication protocols that are typically opaque to a human. A
large body of work has attempted to evaluate the emergent communication via
various evaluation measures, with \emph{compositionality} featuring as a
prominent desired trait. However, current evaluation procedures do not directly
expose the compositionality of the emergent communication. We propose a
procedure to assess the compositionality of emergent communication by finding
the best-match between emerged words and natural language concepts. The
best-match algorithm provides both a global score and a translation-map from
emergent words to natural language concepts. To the best of our knowledge, it
is the first time that such direct and interpretable mapping between emergent
words and human concepts is provided.