论文标题
深度学习中新兴语言的组成特性
Compositional properties of emergent languages in deep learning
论文作者
论文摘要
多学院深度学习系统中的最新发现表明构图语言的出现。这些主张通常是在没有精确分析或测试语言的情况下做出的。在这项工作中，我们分析了两种不同的合作多代理游戏产生的新兴语言，并采用了更精确的组成措施。我们的发现表明，深度学习模型发现的解决方案通常缺乏在抽象层面上推理的能力，因此未能将学习的知识推广到培训分配示例中。讨论了测试组成能力和人类水平概念的出现策略。
Recent findings in multi-agent deep learning systems point towards the emergence of compositional languages. These claims are often made without exact analysis or testing of the language. In this work, we analyze the emergent language resulting from two different cooperative multi-agent game with more exact measures for compositionality. Our findings suggest that solutions found by deep learning models are often lacking the ability to reason on an abstract level therefore failing to generalize the learned knowledge to out of the training distribution examples. Strategies for testing compositional capacities and emergence of human-level concepts are discussed.
