语言产生的内部和外部压力：最小努力，对象恒定性和频率
Internal and external pressures on language emergence: least effort, object constancy and frequency
摘要：
在以前的工作中，人工代理被证明在参考游戏中达到几乎完美的准确性，在这些游戏中，它们必须进行通信以识别图像。然而，由此产生的通信协议很少显示自然语言的显著特征，如组合性。在本文中，我们提出了一些现实的沟通压力来源，以避免这种结果。更具体地说，我们通过一个辅助目标正式化了最小努力原则。此外，我们探索了几个游戏的变种，灵感来自对象恒常性的原则，在其中我们改变图像中的对象的频率，位置和亮度。我们通过组合性指标、诊断分类器和zero-shot评估对其效果进行了广泛的分析。我们的研究结果表明，提出的压力源的结果，在新兴的语言与较少的冗余，更注重高层次的概念信息，更好的概括能力。总的来说，我们的贡献缩小了新兴语言和自然语言之间的差距。
英文摘要：
In previous work, artificial agents were shown to achieve almost perfect accuracy in referential games where they have to communicate to identify images. Nevertheless, the resulting communication protocols rarely display salient features of natural languages, such as compositionality. In this paper, we propose some realistic sources of pressure on communication that avert this outcome. More specifically, we formalise the principle of least effort through an auxiliary objective. Moreover, we explore several game variants, inspired by the principle of object constancy, in which we alter the frequency, position, and luminosity of the objects in the images. We perform an extensive analysis on their effect through compositionality metrics, diagnostic classifiers, and zero-shot evaluation. Our findings reveal that the proposed sources of pressure result in emerging languages with less redundancy, more focus on high-level conceptual information, and better abilities of generalisation. Overall, our contributions reduce the gap between emergent and natural languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
