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.
翻译:句子制作期间的单词顺序选择可以通过前一句开始。 在这项工作中, 我们测试了由多种不同来源驱动的单词机制假设。 使用印地文文本制作集, 我们用正克缓冲模型模拟词汇边际模型, 我们用适应性神经语言模型捕捉到更抽象的合成组合。 我们观察了判决的预言成分, 然后使用后勤回归模型来预测在实体中实际发生了哪些判决, 而不是人为生成的等值值值变量。 我们的结果表明, 单词边际和法系独立的合成模型会影响一系列动词类的互补。 通过显示不同的侧面影响是相互分隔的, 我们的结果支持了一种假设, 即多重不同的认知机制是连接的。