论文标题
“ lazimpa”：懒惰和急躁的神经毒剂学会有效地交流
"LazImpa": Lazy and Impatient neural agents learn to communicate efficiently
论文作者
论文摘要
先前的工作表明，人工神经剂自然会开发出令人惊讶的非效率代码。在涉及扬声器和听众神经网络的参考游戏中，通过离散通道优化准确的传输，这一事实说明了这一点，紧急消息无法实现最佳的长度。此外，频繁的消息往往比很少见的消息更长，这种模式与所有自然语言中观察到的缩写法（ZLA）相反。在这里，我们表明可以出现近距离和ZLA兼容的消息，但前提是说话者和听众都经过修改。因此，我们引入了一个新的通信系统“ Lazimpa”，在该系统中，演讲者越来越懒惰，即避免长长的消息，听众不耐烦，即〜试图尽快猜测预期的内容。
Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a speaker and a listener neural networks optimizing accurate transmission over a discrete channel, the emergent messages fail to achieve an optimal length. Furthermore, frequent messages tend to be longer than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA) observed in all natural languages. Here, we show that near-optimal and ZLA-compatible messages can emerge, but only if both the speaker and the listener are modified. We hence introduce a new communication system, "LazImpa", where the speaker is made increasingly lazy, i.e. avoids long messages, and the listener impatient, i.e.,~seeks to guess the intended content as soon as possible.
