The effect of syntactic priming exhibits three well-documented empirical
properties: the lexical boost, the inverse frequency effect, and the
asymmetrical decay. We aim to show how these three empirical phenomena can be
reconciled in a general learning framework, the hierarchical Bayesian model
(HBM). The model represents syntactic knowledge in a hierarchical structure of
syntactic statistics, where a lower level represents the verb-specific biases
of syntactic decisions, and a higher level represents the abstract bias as an
aggregation of verb-specific biases. This knowledge is updated in response to
experience by Bayesian inference. In simulations, we show that the HBM captures
the above-mentioned properties of syntactic priming. The results indicate that
some properties of priming which are usually explained by a residual activation
account can also be explained by an implicit learning account. We also discuss
the model's implications for the lexical basis of syntactic priming.

通过层级贝叶斯模型，我们展示了语法启动作用的三个经验现象：词汇增强效应、反频率效应和不对称衰减可以在一个通用的学习框架中得到协调。模型通过贝叶斯推断更新语法知识，其中低层级表示动词特定决策的偏见，高层级表示动词特定偏见的抽象偏见的综合。模拟结果显示，该模型能够捕捉到语法启动作用的上述特性，表明通常由残余激活解释的一些启动作用特性也可以由隐式学习解释。我们还讨论了该模型对语法启动作用的词汇基础的影响。

一种语法启动的分层贝叶斯模型

A hierarchical Bayesian model for syntactic priming

We test the hypothesis that discourse predictability influences Hindi
syntactic choice. While prior work has shown that a number of factors (e.g.,
information status, dependency length, and syntactic surprisal) influence Hindi
word order preferences, the role of discourse predictability is underexplored
in the literature. Inspired by prior work on syntactic priming, we investigate
how the words and syntactic structures in a sentence influence the word order
of the following sentences. Specifically, we extract sentences from the
Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constituents of those
sentences, and build a classifier to predict which sentences actually occurred
in the corpus against artificially generated distractors. The classifier uses a
number of discourse-based features and cognitive features to make its
predictions, including dependency length, surprisal, and information status. We
find that information status and LSTM-based discourse predictability influence
word order choices, especially for non-canonical object-fronted orders. We
conclude by situating our results within the broader syntactic priming
literature.

考察了语篇可预测性对印地语句法选择的影响，发现信息状态和 LSTM-based 语篇可预测性对非规范的宾语前置语序选择有影响。

印地文语境可预测性对词序的影响

Discourse Context Predictability Effects in Hindi Word Order

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。