Analyzing the Surprising Variability in Word Embedding StabilityAcross Languages
Table 2: Weights with the highest magnitude in the full regression model on all languages. Negative weights correspond with low stability, and positive weights correspond with high stability.
['WALS Attribute', 'Weight']
Since we see that our two corpora correlate reasonably well, we combine both corpora to build a regression model that includes all of the languages that we have (111 languages). Combining corpora allows us to cover a larger number of languages, and it allows us to generalize across both datasets. If a language is present in both the Wikipedia and the Bible corpus, we average the stabilities from both corpora. We filter out all WALS features that are covered by less than 1% of our languages, leaving us with 35 WALS features. This model has an reasonably high R2 score of 0.5±0.00, indicating that this model fits the data well.