Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context
Table 6: Sarcasm detection performance according to the ensemble methods on the twittervalid dataset.
['[ITALIC] twittervalid', 'Precision', 'Recall', 'F1']
Context Ensemble. We use the baseline (Transformer+BiLSTM+ Maxpooling) and train it without augmenting the training set. F1 scores of the model are better in the order of (a) ensemble with maximum context, (b) ensemble with three contexts and (c) no context. The performance gap with or without context ensemble implies that balancing out the samples in terms of context length is important. On the other hand, the performance gap between (a) and (b) is only 0.006, indicating that the use of older than three recent conversational contexts is scarcely helpful.