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：强大的系统性组合性元学习能力正成为应对当今世界复杂多变任务的重要技能。然而，在提出适应新环境的鲁棒模型时，必须避免对元学习系统做出未经证实的性能断言，这些断言最终往往经不起严格检验。尽管福多尔与派利夏恩曾著名地指出神经网络天生缺乏这种能力——因其无法建模组合表征或结构敏感操作，故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路径。在本立场论文中，我们批判性地重审了这一主张，并指出该组合性元学习框架的局限性。我们的分析表明，现代神经元学习系统仅能在元学习设置的定义极为狭隘且受限的情况下（若有可能）完成此类任务。因此我们认为“福多尔与派利夏恩的遗产”依然存在，迄今为止，神经网络尚未习得类人的系统性组合性。