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

An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games