Deep and Dense Sarcasm Detection
Table 3: Comparison of our proposed approach, the final entry, to structural variants.
['[EMPTY]', 'Headlines']
Experiments surrounding architectural designs are conducted on the proposed dense model to evaluate various features. Residual archetypes marginally outperformed the base CNN but resulted in lower accuracies and increased parameter counts when compared to the dense variant. This suggests low-level feature reuse is indeed significant, given that a ResNet is unable to take advantage of this. Further, depth was found to be beneficial in classification accuracy, resulting in an increase of 1.74%. This, however, was expected based on promising depth investigations across several NLP tasks conducted by Schwenk et al. Keeping the pretrained Glove embeddings static resulted in a notable drop of 2.99%, which can be attributed to the nuanced nature of sarcasm. Pretrained embeddings are trained on large-scale typical language datasets, which will rarely include sarcastic phrases, resulting in word embeddings which do not align with the semantics of sarcasm.