  Guessing games are a prototypical instance of the "learning by interacting"
paradigm. This work investigates how well an artificial agent can benefit from
playing guessing games when later asked to perform on novel NLP downstream
tasks such as Visual Question Answering (VQA). We propose two ways to exploit
playing guessing games: 1) a supervised learning scenario in which the agent
learns to mimic successful guessing games and 2) a novel way for an agent to
play by itself, called Self-play via Iterated Experience Learning (SPIEL).
  We evaluate the ability of both procedures to generalize: an in-domain
evaluation shows an increased accuracy (+7.79) compared with competitors on the
evaluation suite CompGuessWhat?!; a transfer evaluation shows improved
performance for VQA on the TDIUC dataset in terms of harmonic average accuracy
(+5.31) thanks to more fine-grained object representations learned via SPIEL.
