Although passivization is productive in English, it is not completely general -- some exceptions exist (e.g. *One hour was lasted by the meeting). How do English speakers learn these exceptions to an otherwise general pattern? Using neural network language models as theories of acquisition, we explore the sources of indirect evidence that a learner can leverage to learn whether a verb can passivize. We first characterize English speakers' judgments of exceptions to the passive, confirming that speakers find some verbs more passivizable than others. We then show that a neural network language model can learn restrictions to the passive that are similar to those displayed by humans, suggesting that evidence for these exceptions is available in the linguistic input. We test the causal role of two hypotheses for how the language model learns these restrictions by training models on modified training corpora, which we create by altering the existing training corpora to remove features of the input implicated by each hypothesis. We find that while the frequency with which a verb appears in the passive significantly affects its passivizability, the semantics of the verb does not. This study highlight the utility of altering a language model's training data for answering questions where complete control over a learner's input is vital.

翻译：尽管英语中的被动语态具有能产性，但其并非完全普适——存在某些例外情况（例如*One hour was lasted by the meeting）。英语母语者如何习得这些普遍规则中的例外？我们以神经网络语言模型作为习得理论，探究学习者可利用哪些间接证据来源来学习动词能否被动化。首先，我们刻画了英语母语者对被动例外情况的判断，确认说话者认为某些动词比其他动词更易被动化。随后，我们证明神经网络语言模型能够习得与人类表现相似的被动限制，表明语言输入中存在支持这些例外的证据。通过训练经修改训练语料库的模型，我们检验了语言模型如何习得这些限制的两个假说的因果作用——这些修改语料库是通过消除原始训练语料中与各假说相关的输入特征而创建的。研究发现：动词在被动语态中出现的频率对其可被动性有显著影响，而动词的语义则无显著影响。本研究凸显了通过改变语言模型训练数据来解决需要完全控制学习者输入的问题的实用性。