Advances in Neural Information Processing Systems 32 (NIPS 2019)
Publisher
Curran Associates
Publication year
2019
Pages
6290-6300
Abstract
apos;, are trained to play a signaling game. Surprisingly, we find that networks develop an \emph{anti-efficient} encoding scheme, in which the most frequent inputs are associated to the longest messages, and messages in general are skewed towards the maximum length threshold. This anti-efficient code appears easier to discriminate for the listener, and, unlike in human communication, the speaker does not impose a contrasting least-effort pressure towards brevity. Indeed, when the cost function includes a penalty for longer messages, the resulting message distribution starts respecting ZLA. Our analysis stresses the importance of studying the basic features of emergent communication in a highly controlled setup, to ensure the latter will not strand too far from human language. Moreover, we present a concrete illustration of how different functional pressures can lead to successful communication codes that lack basic properties of human language, thus highlighting the role such pressures play in the latter.
Complete citation
Chaabouni, R.; Kharitonov, E.; Dupoux, E.; Baroni, M.. Anti-efficient encoding in emergent communication. In: Wallach, H.; Larochelle, H.; Beygelzimer, A.; d'Alché-Buc, F.; Fox, E.; Garnett, R. (eds.). Advances in Neural Information Processing Systems 32 (NIPS 2019). 1 ed. Vancouver, BC: Curran Associates; 2019. p. 6290-6300.
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