操控语言模型的训练数据以研究句法约束学习：以英语被动化为例
Manipulating language models' training data to study syntactic constraint learning: the case of English passivization
AI总结：
本研究通过操控语言模型训练数据，探讨英语被动化例外的习得机制，发现频率和语义因素共同影响动词被动化能力。
摘要：
自然语言中的语法规则往往带有例外。语言学习者是如何学习这些例外以掌握一般性模式的？本文通过英语被动化的案例研究来探讨这一问题。虽然被动化通常具有较强的生成能力，但存在一些情况下无法应用（例如：*One hour was lasted by the meeting 是不正确的）。使用神经网络语言模型作为语言习得的理论，我们探索了学习者可以利用的间接证据来源，以判断一个动词是否可以被被动化。我们首先描述了英语学习者对被动化例外的判断，并确认了学习者发现某些动词比其他动词更容易被被动化。然后我们显示，神经网络语言模型对动词被动化能力的判断与人类表现相似，这表明这些例外的证据存在于语言输入中。最后，我们测试了两种关于语言模型学习这些限制的证据来源的假设：频率（固着性）和语义（受影响性）。我们通过训练模型在去除、修改或引入特定类型句子的语料库版本中进行测试。我们发现两种假设都得到了支持：固着性和受影响性对动词被动化能力有独立贡献。从方法论角度来看，本研究强调了通过改变语言模型的训练数据来回答问题的实用性，尤其是在需要完全控制学习者输入的情况下。
英文摘要：
Grammatical rules in natural languages are often characterized by exceptions. How do language learners learn these exceptions to otherwise general patterns? Here, we study this question through the case study of English passivization. While passivization is in general quite productive, there are cases where it cannot apply (cf. the following sentence is ungrammatical: *One hour was lasted by the meeting). Using neural network language models as theories of language acquisition, we explore the sources of indirect evidence that a learner can leverage to learn whether a verb can be passivized. We first characterize English speakers' judgments of exceptions to the passive, and confirm that speakers find some verbs more passivizable than others. We then show that a neural network language model's verb passivizability judgments are largely similar to those displayed by humans, suggesting that evidence for these exceptions is available in the linguistic input. Finally, we test two hypotheses as to the source of evidence that language models use to learn these restrictions: frequency (entrenchment) and semantics (affectedness). We do so by training models on versions of the corpus that have had sentences of the types implicated by each hypothesis removed, altered, or introduced. We find support for both hypotheses: entrenchment and affectedness make independent contributions to a verb's passivizability. From a methodological point of view, this study highlights the utility of altering a language model's training data for answering questions where complete control over a learner's input is vital.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
