# Cross-Lingual Cross-Domain Transfer Learning for
## 4 Experiments
### Results on Rumor Detection in English--Stage 2 and 3  
Building on the models of the three auxiliary tasks presented earlier, we proceeded to evaluate the 80 experimental settings (Section 3) outlined before to study their performance on the task of rumor detection in English. A standard training/evaluation loop was set up, incorporating testing on each epoch. All models run this loop for 1000 epochs. This choice denotes a large number of epochs in which the performance of each model can be adequately evaluated because the training has been running long enough and no further improvements are possible. In practical terms, this makes development easier than implementing techniques such as early stopping. After training all CLMs, we study their behavior and performance.  
Regarding their behavior, we note whether a setting presents learning or not, which is observed graphically in the score/loss curve of each setting. In addition, we note the epoch in which we consider the model has stopped learning, alongside its Macro-F1 score at that specific point; note that this might not necessarily be the highest score achieved during training, although we also consider the best score overall. Note that all Macro-F1 scores refer to the test set evaluated after training at every epoch.  
After evaluating all CLMs, the following conclusions were drawn: of 80 total settings, 55 showed evidence of learning, while 25 settings were excluded due to a lack of any evidence of learning. This initial assessment was based on the information loss curves described above. Figure 6 presents a general overview of all the models that learn (that is, the 55 settings, 68.75 % of the 80 settings) and their performance. In the figure, the green line indicates the 0.5 score to use as a reference value for the score of each setting. In addition, for the 55 settings that learn, all their results have a Macro-F1 score greater than 0.7. More specifically,  
* A proportion of 5.45% (3 settings) obtain a score Macro-F1 in the interval \([0.7,0.8)\).
* A proportion of 54.55% (30 settings) obtain a score Macro-F1 in the interval \([0.8,0.9)\).
* A proportion of 40.00% (22 settings) obtain a score Macro-F1 over 0.9.  
Figure 6: Best Macro-F1 results on the test set for CLM models fine-tuned on the Twitter16 dataset, over a fixed amount of 1000 epochs. The \(x\) axis presents the number of settings \(S_{i}\), and the \(y\) axis shows the Macro-F1 scores. The horizontal bar delimits the barrier of the 0.5 score. The number over each bar indicates in which epoch during training the best score is obtained for each setting. The hatched bars correspond to the three best results.  
Furthermore, in terms of the 55 settings that demonstrated learning, each achieved a Macro-F1 score greater than 0.7. The best-performing settings are detailed in Table 5.  
As Table 5 shows, the three best results have Macro-F1 scores greater than 0.9. We highlight the setting \(S_{58}\) since it uses the three auxiliary tasks. This suggests that the number of tasks used to train a CLM has a noticeable impact on its performance.  
When considering the models in the best-fit CLM training schemes, with three models, we see that  
* Bot detection appears in all the best results, such as the first or second model. This suggests that this task is more valuable as a starting point for CLM training.
* More specifically, bot detection in English is the most commonly used first model or task.
* Regarding the third model/task, from the above analysis, it is clear that stance classification appears less frequently (only in one setting in the top three results).