Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world. However, in presenting models for robust adaptation to novel environments, it is important to refrain from making unsupported claims about the performance of meta-learning systems that ultimately do not stand up to scrutiny. While Fodor and Pylyshyn famously posited that neural networks inherently lack this capacity as they are unable to model compositional representations or structure-sensitive operations, and thus are not a viable model of the human mind, Lake and Baroni recently presented meta-learning as a pathway to compositionality. In this position paper, we critically revisit this claim and highlight limitations in the proposed meta-learning framework for compositionality. Our analysis shows that modern neural meta-learning systems can only perform such tasks, if at all, under a very narrow and restricted definition of a meta-learning setup. We therefore claim that `Fodor and Pylyshyn's legacy' persists, and to date, there is no human-like systematic compositionality learned in neural networks.

翻译：强大的元学习能力正成为应对当今世界复杂多变任务的关键技能，然而在提出适应新环境的稳健模型时，应避免对元学习系统做出经不起严格检验的性能断言。尽管福多与派利夏恩曾著名地指出神经网络天生缺乏组合表征或结构敏感操作的能力，因此不能作为人类心智的可行模型，但莱克与巴罗尼近期提出元学习可作为实现组合性的途径。在本立场论文中，我们批判性地重新审视了这一主张，并指出该元学习框架在实现组合性方面的局限性。我们的分析表明，现代神经元学习系统仅能在极其狭隘受限的元学习设定定义下执行此类任务（如果确实能执行的话）。因此我们认为“福多与派利夏恩的遗产”依然存在，迄今为止神经网络尚未习得类人的系统组合性。