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 \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.

研究了当两个神经网络，即 “说话人” 和 “听众” 被训练玩信号游戏时，是否会产生类似 Zipf 定律的现象，然而我们发现，这些神经网络没有按照 Zipf 定律的方式编码，即更常见的输入被分配到更长的消息中，这种反效率的编码方式对于监听者更容易进行区分，并且与人类沟通不同的是，说话人不会对简洁性施加压力，同时，当成本函数包括对更长消息的惩罚时，结果消息分布开始遵循 Zipf 定律。因此，该研究强调了在高度控制的设置中研究新兴沟通的基本特征的重要性，以确保后者不会偏离人类语言。