Modern work on the cross-linguistic computational modeling of morphological
inflection has typically employed language-independent data splitting
algorithms. In this paper, we supplement that approach with language-specific
probes designed to test aspects of morphological generalization. Testing these
probes on three morphologically distinct languages, English, Spanish, and
Swahili, we find evidence that three leading morphological inflection systems
employ distinct generalization strategies over conjugational classes and
feature sets on both orthographic and phonologically transcribed inputs.

跨语言计算建模的形态变化研究通常采用与语言无关的数据分割算法。本文采用特定语言的探测器来测试形态变化的一些普遍规律。通过在英语、西班牙语和斯瓦希里语这三种形态学上有明显区别的语言上测试这些探测器，我们发现了三种主要的形态变化系统在屈折类和特征集上都采用了不同的普遍规律策略，无论是在拼写还是在音标输入上。

探索语言揭示词形概括

Exploring Linguistic Probes for Morphological Generalization

It is widely accepted in the mode connectivity literature that when two
neural networks are trained similarly on the same data, they are connected by a
path through parameter space over which test set accuracy is maintained. Under
some circumstances, including transfer learning from pretrained models, these
paths are presumed to be linear. In contrast to existing results, we find that
among text classifiers (trained on MNLI, QQP, and CoLA), some pairs of
finetuned models have large barriers of increasing loss on the linear paths
between them. On each task, we find distinct clusters of models which are
linearly connected on the test loss surface, but are disconnected from models
outside the cluster -- models that occupy separate basins on the surface. By
measuring performance on specially-crafted diagnostic datasets, we find that
these clusters correspond to different generalization strategies: one cluster
behaves like a bag of words model under domain shift, while another cluster
uses syntactic heuristics. Our work demonstrates how the geometry of the loss
surface can guide models towards different heuristic functions.

使用 MNLI、QQP 和 CoLA 三个任务的文本分类模型为研究对象，发现了线性路径上增加的损失不断增加的现象，并探究了不同模型之间的泛化策略差异与损失表面几何形状之间的关系。