“LazImpa”: Lazy and Impatient neural agents learn to communicate efficiently
“LazImpa”: Lazy and Impatient neural agents learn to communicate efficiently
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“LazImpa”：懒惰和不耐烦的神经代理学习有效沟通
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Emmanuel Dupoux
中科院分区：
文献类型：
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作者：
Mathieu Rita;Rahma Chaabouni;Emmanuel Dupoux
以前的工作已经表明，人工神经代理自然会产生令人惊讶的低效代码。事实说明了这一点，在涉及说话人和听话人的指涉游戏中，优化离散信道上的准确传输的神经网络，紧急消息无法达到最佳长度。此外，频繁的消息往往比不频繁的消息更长，这与所有自然语言中观察到的Zipf缩写定律(ZLA)背道而驰。在这里，我们表明，只有在说话人和听话人都被修改的情况下，才能出现接近最佳的和ZLA兼容的消息。因此，我们引入了一种新的通信系统LazImpa，其中说话者变得越来越懒惰，即避免长消息，而听者不耐烦，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.