How can children acquire native-level syntax from limited input? According to the Poverty of the Stimulus Hypothesis (PoSH), the linguistic input children receive is insufficient to explain certain generalizations that are robustly learned; innate linguistic constraints, many have argued, are thus necessary to explain language learning. Neural language models, which lack such language-specific constraints in their design, offer a computational test of this longstanding (but controversial) claim. We introduce \poshbench, a training-and-evaluation suite targeting question formation, islands to movement, and other English phenomena at the center of the PoSH arguments. Training Transformer models on 10--50M words of developmentally plausible text, we find indications of generalization on all phenomena even without direct positive evidence -- yet neural models remain less data-efficient and their generalizations are weaker than those of children. We further enhance our models with three recently proposed cognitively motivated inductive biases. We find these biases improve general syntactic competence but not \poshbench performance. Our findings challenge the claim that innate syntax is the only possible route to generalization, while suggesting that human-like data efficiency requires inductive biases beyond those tested here.

翻译：儿童如何从有限的输入中习得母语水平的句法？根据刺激贫乏假说，儿童接收的语言输入不足以解释某些被稳健习得的语言概括现象；因此许多学者认为，先天的语言约束对于解释语言学习是必要的。神经语言模型在设计上缺乏此类语言特异性约束，为这一长期存在（但具争议性）的论断提供了计算验证。我们提出\poshbench——一个针对疑问句形成、移位岛约束及其他处于刺激贫乏论证核心的英语语言现象的训练与评估套件。通过在1000万至5000万单词量级（符合儿童语言发展实际规模）的文本上训练Transformer模型，我们发现模型在所有语言现象上均显示出泛化迹象（即使缺乏直接正面证据），但神经模型的数据效率仍低于儿童，其泛化能力也弱于儿童。我们进一步为模型引入了三种近期提出的认知驱动归纳偏置。实验表明这些偏置能提升模型的整体句法能力，但并未改善\poshbench性能。我们的研究结果对"先天句法是实现泛化的唯一途径"这一论断提出挑战，同时表明要实现类人的数据效率，需要超越本文所测试范围的归纳偏置机制。