论文标题
新兴的语言概括和获取速度与组成性无关
Emergent Language Generalization and Acquisition Speed are not tied to Compositionality
论文作者
论文摘要
当神经药物交流以解决共同任务时，出现的离散语言的研究通常会寻找组成结构的证据。这是因为期望这种结构将使语言能够更快地被代理人获取，并使它们能够更好地概括。我们认为，这些有益的特性仅与组成性密切相关。在两个实验中，我们证明，根据任务，非构成语言可能表现出相等或更好的概括性能和获取速度，而不是组成速度。该地区的进一步研究应该更清楚地了解组成性的期望以及后者将如何导致它们。
Studies of discrete languages emerging when neural agents communicate to solve a joint task often look for evidence of compositional structure. This stems for the expectation that such a structure would allow languages to be acquired faster by the agents and enable them to generalize better. We argue that these beneficial properties are only loosely connected to compositionality. In two experiments, we demonstrate that, depending on the task, non-compositional languages might show equal, or better, generalization performance and acquisition speed than compositional ones. Further research in the area should be clearer about what benefits are expected from compositionality, and how the latter would lead to them.
