Abstract
Emergent Communication (EC) provides a unique window into the language systems that emerge autonomously when agents are trained to jointly achieve shared goals. However, it is difficult to interpret EC and evaluate its relationship with natural languages (NL). This study employs unsupervised neural machine translation (UNMT) techniques to decipher ECs formed during referential games with varying task complexities, influenced by the semantic diversity of the environment. Our findings demonstrate UNMT's potential to translate EC, illustrating that task complexity characterized by semantic diversity enhances EC translatability, while higher task complexity with constrained semantic variability exhibits pragmatic EC, which, although challenging to interpret, remains suitable for translation. This research marks the first attempt, to our knowledge, to translate EC without the aid of parallel data.
Abstract (translated)
新兴通信（EC）为了解当智能体被训练以共同实现共享目标时自主形成的语言系统提供了一个独特的窗口。然而，很难解释EC并评估其与自然语言(NL)的关系。本研究采用无监督神经机器翻译(UNMT)技术来解析在具有不同任务复杂度的指称游戏中形成的EC，这些游戏受到环境语义多样性的影响。我们的研究结果表明，UNMT有潜力将EC进行翻译，并展示了以下发现：由语义多样性定义的任务复杂性增强了EC的可译性；而更高任务复杂性且语义变化受限的情况下，则形成了具有实用性的EC，尽管这种情况下解释起来较为困难，但仍然适合于翻译。据我们所知，这项研究是首次尝试在没有平行数据辅助的情况下将EC进行翻译的研究。
URL
https://arxiv.org/abs/2502.07552
