Identifying linguistic differences between dialects of a language often
requires expert knowledge and meticulous human analysis. This is largely due to
the complexity and nuance involved in studying various dialects. We present a
novel approach to extract distinguishing lexical features of dialects by
utilizing interpretable dialect classifiers, even in the absence of human
experts. We explore both post-hoc and intrinsic approaches to interpretability,
conduct experiments on Mandarin, Italian, and Low Saxon, and experimentally
demonstrate that our method successfully identifies key language-specific
lexical features that contribute to dialectal variations.

通过使用可解释的方言分类器，无论是否有人类专家，我们提供了一种提取方言区别词汇特征的新方法，并在普通话、意大利语和低萨克森语上进行实验，实验证明我们的方法成功地识别了导致方言变异的关键语言特定词汇特征。

通过可解释的方言分类器提取方言的词汇特征

Extracting Lexical Features from Dialects via Interpretable Dialect  Classifiers

This paper analyses the degree to which dialect classifiers based on
syntactic representations remain stable over space and time. While previous
work has shown that the combination of grammar induction and geospatial text
classification produces robust dialect models, we do not know what influence
both changing grammars and changing populations have on dialect models. This
paper constructs a test set for 12 dialects of English that spans three years
at monthly intervals with a fixed spatial distribution across 1,120 cities.
Syntactic representations are formulated within the usage-based Construction
Grammar paradigm (CxG). The decay rate of classification performance for each
dialect over time allows us to identify regions undergoing syntactic change.
And the distribution of classification accuracy within dialect regions allows
us to identify the degree to which the grammar of a dialect is internally
heterogeneous. The main contribution of this paper is to show that a rigorous
evaluation of dialect classification models can be used to find both variation
over space and change over time.

本文分析了基于句法表示的方言分类器在空间和时间上的稳定程度，并构建了一个测试集，评估了 12 种英语方言在 3 年内随时间变化的分类准确性和语法变化速率。通过在语用建构语法范式（CxG）中制定的句法表示，可以识别在时间和空间上发生语法变化的地区。本文的主要贡献在于表明严格评估方言分类模型可用于发现空间上的变异和时间上的变化。