All-in-one: Multi-task Learning for Rumour Verification
Table 4: Per event and per-class results for multi-task learning approach with 3 tasks on PHEME 5 events.
['MTL3 5 events', '[BOLD] MacroF', '[BOLD] Accuracy', '[BOLD] TRUE', '[BOLD] FALSE', '[BOLD] Unverified']
We have analysed similar performance breakdown tables for other models but omit them here because of space constraints. All models tend to predict the majority class (true) the best. As the Ferguson event is strongly dominated by unverified rumours, it is the only event with high performance on the unverified class. Single task models ( NileTMRG*, branchLSTM) are better at identifying false than unverified rumours, whereas multitask models (MTL2, MTL3) are better at identifying unverified than false rumours.