神经网络仍无法解释人类语言的系统性，挑战未解。
Fodor and Pylyshyn's Systematicity Challenge Still Stands
- 用元学习构建的神经网络模型仍难处理分布外规则
- 模型在训练范围内也表现出非系统性行为
- 论文质疑神经网络对语言系统性的解释能力
近年来神经网络生成类人语言的成功引发了认知科学界的广泛讨论，许多研究者认为经典的人类认知难题和人工智能挑战已被解决。其中最具代表性的是杰里·福多与泽诺·皮利什延提出的系统性问题：人类对语言的理解具有双向依赖关系，例如能理解“约翰看见玛丽”就必然能理解“玛丽看见约翰”。符号系统可自然解释这种系统性，而神经网络则缺乏直接解释。近期有文章声称，布伦登·莱克与马尔科·巴罗尼提出的组合性元学习协议已实现并解释了人类系统性。本文指出该结论为时过早：实验发现其模型在略超出训练数据分布的规则上表现不佳，且在部分同分布任务中仍呈现非系统性行为。因此，福多与皮利什延对神经网络的挑战仍未被克服。
原文摘要 · Abstract (English)
The recent successes of neural networks producing human-like language have caused significant stir in cognitive science, with many researchers arguing that classical puzzles about human cognition and challenges to artificial intelligence are being solved by neural networks. A notable case is the argument from systematicity due to Jerry Fodor and Zenon Pylyshyn, argues that humans display systematic biconditional dependencies. For example, someone can understand the sentence "John saw Mary" just in case that they understand the sentence "Mary saw John." Symbolic systems explain this systematicity of language and thought, while neural networks offer no immediate explanation. Several recent articles argue that this challenge has now been met by neural networks. In particular, Brenden Lake and Marco Baroni argue that their meta-learning for compositionality protocol matches and perhaps explains human systematicity. We demonstrate that these conclusions are premature. Among other results, we found that their model struggles to learn rules that are even slightly out of distribution compared to their training data. Furthermore, the model behaves unsystematically even on many within-distribution problems. We conclude that Fodor and Pylyshyn's challenge to neural networks remains unmet.
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