While language is a complex adaptive system, most work on syntactic variation
observes a few individual constructions in isolation from the rest of the
grammar. This means that the grammar, a network which connects thousands of
structures at different levels of abstraction, is reduced to a few disconnected
variables. This paper quantifies the impact of such reductions by
systematically modelling dialectal variation across 49 local populations of
English speakers in 16 countries. We perform dialect classification with both
an entire grammar as well as with isolated nodes within the grammar in order to
characterize the syntactic differences between these dialects. The results
show, first, that many individual nodes within the grammar are subject to
variation but, in isolation, none perform as well as the grammar as a whole.
This indicates that an important part of syntactic variation consists of
interactions between different parts of the grammar. Second, the results show
that the similarity between dialects depends heavily on the sub-set of the
grammar being observed: for example, New Zealand English could be more similar
to Australian English in phrasal verbs but at the same time more similar to UK
English in dative phrases.

该研究利用整个语法以及语法中的独立节点对方言之间的句法差异进行了系统建模，结果表明句法变异的重要部分是语法不同部分之间的相互作用，并且方言之间的相似性严重依赖于被观察的语法子集。

跨语法的句法变异：建模复杂自适应系统

Syntactic Variation Across the Grammar: Modelling a Complex Adaptive  System

Pre-trained language models have achieved huge success on a wide range of NLP
tasks. However, contextual representations from pre-trained models contain
entangled semantic and syntactic information, and therefore cannot be directly
used to derive useful semantic sentence embeddings for some tasks. Paraphrase
pairs offer an effective way of learning the distinction between semantics and
syntax, as they naturally share semantics and often vary in syntax. In this
work, we present ParaBART, a semantic sentence embedding model that learns to
disentangle semantics and syntax in sentence embeddings obtained by pre-trained
language models. ParaBART is trained to perform syntax-guided paraphrasing,
based on a source sentence that shares semantics with the target paraphrase,
and a parse tree that specifies the target syntax. In this way, ParaBART learns
disentangled semantic and syntactic representations from their respective
inputs with separate encoders. Experiments in English show that ParaBART
outperforms state-of-the-art sentence embedding models on unsupervised semantic
similarity tasks. Additionally, we show that our approach can effectively
remove syntactic information from semantic sentence embeddings, leading to
better robustness against syntactic variation on downstream semantic tasks.

ParaBART 是一个语义句子嵌入模型，使用基于源句子和目标释义之间语义相似性差异的语法指导解开基于预训练语言模型的嵌入中的语法和语义信息的工作。该方法在英语分析上表现出色，能有效的从语义句子嵌入中删除语法信息，提高对下游语义任务的鲁棒性。