Bidirectional Recurrent Models for Offensive Tweet Classification
Table 1: Holdout-validation macro F1 scores and accuracy of all models for the three different tasks.
['[EMPTY]', '[BOLD] Task A  [BOLD] F1', '[BOLD] Task A  [BOLD] Acc', '[BOLD] Task B  [BOLD] F1', '[BOLD] Task B  [BOLD] Acc', '[BOLD] Task C  [BOLD] F1', '[BOLD] Task C  [BOLD] Acc']
For most tasks, the most simple biLSTM model outperformed the other architectures, with the more complex biGRU⊕biLSTM closely following. We learned that, at least for this task and data set, more complex models did not necessarily result in better performance. The biLSTM model’s macro F1 scores on the OffensEval private test set were 0.77 for Task A, 0.64 for Task B and 0.52 for Task C, In hindsight, however, the model submitted was not optimal as it was not re-trained on the entire dataset and therefore only used 80% of the training data. We speculate that re-training the model on 100% of the provided dataset would have yielded significantly better results.