Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context
Table 3: Sarcasm detection performance on the validation set. The upper and lower part of the table respectively denote the performance before and after data augmentation is applied. We set the context length to 3 for all models.
['Metric dataset', 'Precision  [ITALIC] twittervalid', 'Precision  [ITALIC] redditvalid', 'Recall  [ITALIC] twittervalid', 'Recall  [ITALIC] redditvalid', 'F1  [ITALIC] twittervalid', 'F1  [ITALIC] redditvalid']
Pooling Layers. When coupled with BiLSTM, NeXtVLAD achieves better performance than max, and mean pooling methods.