Reasoning with Sarcasm by Reading In-between
Table 2: Experimental Results on Tweets datasets. Best result in is boldface and second best is underlined. Best performing baseline is in italics.
['Model', 'Debates (IAC-V1) P', 'Debates (IAC-V1) R', 'Debates (IAC-V1) F1', 'Debates (IAC-V1) Acc', 'Debates (IAC-V2) P', 'Debates (IAC-V2) R', 'Debates (IAC-V2) F1', 'Debates (IAC-V2) Acc']
We observe that our proposed SIARN and MIARN models achieve the best results across all six datasets. The relative improvement differs across domain and datasets. On the Tweets dataset from Ptáček et al. On the other Tweets dataset from Riloff et al. Our proposed SIARN and MIARN models achieve very competitive performance on the Reddit datasets, with an average of ≈2% margin improvement over the best baselines. Notably, the baselines we compare against are extremely competitive state-of-the-art neural network models. This further reinforces the effectiveness of our proposed approach. Additionally, the performance improvement on Debates (long text) is significantly larger than short text (i.e., Twitter and Reddit). For example, MIARN outperforms GRNN and CNN-LSTM-DNN by ≈8%−10% on both IAC-V1 and IAC-V2. At this note, we can safely put RQ1 to rest.