Reviews: Anti-efficient encoding in emergent communication

This paper provides a focused study of the distribution of message lengths in an emergent communication task. A Lewis-type signaling game is constructed in which referents are generated from a power-law distribution. RNN "speaker" and "listener" models are constructed to communicate via a discrete channel (with variable vocabulary size and max length) and trained to maximize success at the signaling game using a vanilla policy gradient algorithm. It is observed that more frequent referents are associated with *longer* messages from the speaker agent. This is in contrast to natural language (exemplified by corpus data from English and Arabic and two simple computational models).