Anti-efficient encoding in emergent communication

Neural Information Processing Systems (NeurIPS)


Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human language. One fundamental characteristic of the latter, known as Zipf’s Law of Abbreviation (ZLA), is that more frequent words are efficiently associated to shorter strings. We study whether the same pattern emerges when two neural networks, a “speaker” and a “listener”, are trained to play a signaling game. Surprisingly, we find that networks develop an 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 depart 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.

Related Publications

All Publications

NAACL - June 6, 2021

Deep Learning on Graphs for Natural Language Processing

Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li

ICASSP - June 6, 2021

On the Predictability of HRTFs from Ear Shapes Using Deep Networks

Yaxuan Zhou, Hao Jiang, Vamsi Krishna Ithapu

CoRL - December 1, 2020

Auxiliary Tasks Speed Up Learning PointGoal Navigation

Joel Ye, Dhruv Batra, Erik Wijmans, Abhishek Das

ACL - July 7, 2020

CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant

Kavya Srinet, Yacine Jernite, Jonathan Gray, Arthur Szlam

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy