Analyzing the Surprising Variability in Word Embedding StabilityAcross Languages
Table 3: Significant weights in the morphology regression model. Negative weights correspond with low stability, and positive weights correspond with high stability.
['WALS Attribute', 'Weight']
We train a regression model taking as input these properties for all languages, and predicting the average stability of a language. This allows us to see how these specific properties of interest relate to stability. This model gets an R2 score of 0.16±0.00. While this model has a lower R2 score than the full model, this model shows that there is still a connection between morphology and stability. Morphology is related to average stability of a language. While the morphology model does not perform as well as the full model, it does indicate that morphological features are related to stability. Perhaps intuitively, more concatenative languages tend to be less stable than more isolating languages.