Back to the Future – Sequential Alignment of Text Representations
Table 3: F1 score in SDQC task of RumourEval-2019 [7]
['[BOLD] Test year', '[BOLD] All', '[BOLD] Same', '[BOLD] Prev', '[BOLD] Unsup.', '[BOLD] Semi-sup.', '[BOLD] Unsup. Unb.', '[BOLD] S. Unb.', '[BOLD] S. Unb. w/Clst']
In this data set, there is a large class imbalance, with a large majority of comment tweets and few support or deny tweets. To address this, we over-sample the minority classes. Afterwards, a SVM with RBF is trained and we test on unlabelled tweets for the current rumour. As with the previous tasks, semi-supervised alignment generally helps, except for in the charliehebdo rumour.