This alone-that cross-domain performance can be so strikingly worse-is a significant result, providing the first estimate of how performance degrades across these domains for this task.
when we train an identically parameterized model on the training partition of the literary data and evaluate it on the literary test partition, performance naturally improves substantially to an F-score of 68.3.