CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Table 2: Comparison of CASCADE with state-of-the-art networks and baselines on multiple versions of the SARC dataset. We assert significance when p<0.05 under paired-t test. Results comprise of 10 runs with different initializations. The bottom row shows the absolute difference with respect to the CUE-CNN system.
['Models', 'Main', 'Main', 'Main', 'Main', 'Pol', 'Pol']
CASCADE manages to achieve major improvement across all datasets with statistical significance. The lowest performance is obtained by the Bag-of-words approach whereas all neural architectures outperform it. Amongst the neural networks, the CNN baseline receives the least performance. CASCADE comfortably beats the state-of-the-art neural models CNN-SVM and CUE-CNN. Its improved performance on the Main imbalanced dataset also reflects its robustness towards class imbalance and establishes it as a real-world deployable network. Since CUE-CNN generates its user embeddings using a method similar to the ParagraphVector, we test the importance of personality features being included in our user profiling. As seen in the table, CASCADE without personality features drops in performance to a range similar to CUE-CNN. This suggests that the combination of stylometric and personality features are indeed crucial for the improved performance of CASCADE.