Word order choices during sentence production can be primed by preceding
sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is
driven by multiple different sources. Using a Hindi corpus of text productions,
we model lexical priming with an n-gram cache model and we capture more
abstract syntactic priming with an adaptive neural language model. We permute
the preverbal constituents of corpus sentences, and then use a logistic
regression model to predict which sentences actually occurred in the corpus
against artificially generated meaning-equivalent variants. Our results
indicate that lexical priming and lexically-independent syntactic priming
affect complementary sets of verb classes. By showing that different priming
influences are separable from one another, our results support the hypothesis
that multiple different cognitive mechanisms underlie priming.

本文使用 Hindi 语料库和适应性神经语言模型，模拟词汇和语言结构的启动作用，并通过对预先构建的语料与人工生成的语料在句子成分上的差异进行逻辑回归分析，发现不同的启动机制独立并互补地作用于动词类别。