Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment
Table 2: Italy’s Spearman correlation results with total and daily case count prediction for mBERT and LASER (Embed). Time settings are defined in 2.2. We bold the highest correlations within each case setting.
['Cases', 'Embed', 'Time Setting I', 'Time Setting II', 'Time Setting III', 'Time Setting IV']
Through manual tuning, we find our strongest model (polynomial kernel) contained the keyword lockdown (in English) and averaged tweet representations from mBERT for the total case scenario. When aligning to new cases, the best model (sigmoid kernel) contained keyword lockdown (in English) and max-pooled LASER embeddings. While mBERT and LASER provide very little difference in alignment to total cases, LASER is noticeably stronger in the new case setting, particularly in time II. For the total case setting, our predictions show strong alignment with ground truth, which is monotonically increasing, in all time settings. When measuring against new daily cases, the correlations are not as significant in time II. As a result, there is a distribution shift when training on February data only (tail of the distribution) and testing in both March and April.