Table 5 Comparison with Uniform Attention as the Adversary mechanism
From: Readers’ affect: predicting and understanding readers’ emotions with deep learning
Approach | Acc@1 (%)\(\uparrow\) | \(\hbox {AP}_{{\mathrm{document}}}\uparrow\) | \(\hbox {AP}_{{\mathrm{emotion}}}\uparrow\) | \(\hbox {RMSE}_{{\mathrm{D}}}\downarrow\) | \(\hbox {WD}_{\mathrm{D}}\downarrow\) |
|---|---|---|---|---|---|
REN-10k | |||||
Uniform Attention | 54.36 | 0.6963 | 0.4019 | 0.2200 | 0.1125 |
Bi-LSTM + Attention (Baseline-Our Method) | 60.55 | 0.7994 | 0.5596 | 0.1500 | 0.0812 |
RENh-4k | |||||
Uniform Attention | 46.87 | 0.6156 | 0.3515 | 0.2435 | 0.1357 |
Bi-LSTM + Attention (Baseline-Our Method) | 50.50 | 0.6499 | 0.4054 | 0.2301 | 0.1220 |
SemEval-2007 | |||||
Uniform Attention | 46.98 | 0.6490 | 0.5255 | 0.2050 | 0.1105 |
Bi-LSTM + Attention (Baseline-Our Method) | 52.60 | 0.7140 | 0.5506 | 0.1700 | 0.0915 |