Deep and Dense Sarcasm Detection
Table 2: Comparison of our approach, dweNet, with state-of-the-art models and simple baselines on two versions of the SARC dataset. Blue entries indicate results rivaled by our network and dashed lines where no results were reported.
['[EMPTY]', 'Main  [ITALIC] Accuracy', 'Main  [ITALIC] F1', 'Pol  [ITALIC] Accuracy', 'Pol  [ITALIC] F1']
Here, we present the results of our model, dweNet, compared to that of other state-of-the-art approaches. We can see that all proposed approaches - including ours - outperform the simple BOW and CNN baselines. Entries written in blue represent results which are rivaled by our network. Each model - except the full CASCADE implementation - is challenged by our approach; this is interesting considering our method makes no use of context, nor meta-data. The full CASCADE model makes the most use of these extrinsic features; supporting the need for background information. However, the competitive results obtained by our model suggests that additional information is indeed available in the isolated utterance, allowing it to outperform a network which made use of pretrained models extracting emotion, sentiment and personality features.