Metric Learning for Dynamic Text Classification
Table 1: Test accuracy for each dataset and method. Columns indicate the number of examples per label nfine used in the fine tuning stage. In all cases, the prototypical models outperform the baseline. The hyperbolic model performs best in the low data regime, but both metrics perform comparably when data is abundant.
['Dataset', 'Model', '[ITALIC] nfine=5', '[ITALIC] nfine=10', '[ITALIC] nfine=20', '[ITALIC] nfine=100']
S4SS0SSS0Px5 Results: The SENT dataset shows performance in the case where completely new labels are added during fine tuning. In the NEWS and WOS datasets new labels originate from the splits of old labels. This is consistent with our hypothesis (and previous work) that hyperbolic geometry is well suited for hierarchical data. Interestingly, the hyperbolic model also performs better on the non-hierarchical SENT dataset when given few examples, which implies that certain metric spaces may be generally stronger in the low-resource setting. In the high data regime, however, both prototypical models perform comparably.