# Hierarchical Transformer Network for Utterance-Level Emotion Recognition
## 5 Result Analysis
### Comparison with Baselines  
Our proposed HiTransformer-s obtains competitive results compared with the state-of-the-art methods in terms of the macro-F1 score. Specifically, HiTransformer-s obtains a 3.94% absolute improvement over EmoryNLP, and HiTransformer-s is 5.22% and 4.07% less than IDEA for Friends and EmotionPush, respectively. In addition, for Friends, HiTransformer-s obtains 0.58% improvement compared with the best performance in the past in terms of WA, and 9.12% less than the best performance from IDEA in terms of UWA. However, HiTransformer-s obtains a 0.58% improvement compared with IDEA in terms of WA. For EmotionPush, HiTransformer-s is 1.28% lower than IDEA in terms of WA and 21.87% lower than IDEA in terms of UWA. This can be attributed to different modifications of model IDEA based on different data sets, and pretraining was performed on Twitter data. IDEA includes two different BERT models to deal with two different datasets, and pretraining was performed with different data sets. Although IDEA has done well in the above two datasets, the method has not done well in terms of generality. For EmoryNLP, HiTransformer-s obtains 1.88% and 2.37% absolute improvement in terms of WA and UWA, respectively. To make the results more intuitive and prominent, in Figure 5, we compare the \(Macro-F1\) values of several excellent methods for the three datasets. The above results demonstrate the superior power of HiTransformer-s and HiTransformer.  
Figure 5: **(a)** The \(Macro-F1\) of several excellent methods. bL: \(\mathrm{bLSTM}_{+}\), \(\mathrm{bG}\): \(\mathrm{bcGRU}\), \(\mathrm{C:CoDE}_{mid}\),\(\mathrm{PT-C}\): \(\mathrm{PT-CoDE}_{mid}\), \(\mathrm{HT}\): HiTransformer, \(\mathrm{HT-s}\): HiTransformer-s; **(b)** Description of the difference between the results of HiTransformer-s and HiTransformer applied to three datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Friends**} & \multicolumn{3}{c}{**EmotionPush**} & \multicolumn{3}{c}{**EmoryNLP**} \\ \cline{2-10}  & **Mac-F1** & **WA** & **UWA** & **Mac-F1** & **WA** & **UWA** & **Mac-F1** & **WA** & **UWA** \\ \hline SA-BiLSTM & - & 79.8 & 59.6 & - & 87.7 & 55.0 & - & - & - \\ CNN-DCNN & - & 67.0 & 62.5 & - & 75.7 & 62.5 & - & - & - \\ \(\mathrm{bclSTM}_{+}\) & 63.1 & 79.9 & 63.3 & 60.3 & 84.8 & 57.9 & 25.5 & 33.5 & 27.6 \\ \(\mathrm{bcGRU}\) & 62.4 & 77.6 & 66.1 & 60.5 & 84.6 & 56.9 & 26.1 & 33.1 & 27.4 \\ \(\mathrm{CoDE}_{mid}\) & 62.4 & 78.0 & 65.3 & 60.3 & 84.2 & 58.5 & 26.7 & 34.7 & 28.8 \\ \(\mathrm{PT-CoDE}_{mid}\) & 65.9 & 81.3 & 66.8 & 62.6 & 84.7 & 60.4 & 29.1 & 36.1 & 30.3 \\ HiGRU & - & 74.4 & 67.2 & - & 73.8 & 66.3 & - & - & - \\ HiGRU-f & - & 71.3 & 68.4 & - & 73.0 & 66.9 & - & - & - \\ HiGRU-sf & - & 74.0 & 68.9 & - & 73.0 & 68.1 & - & - & - \\ IDEA & **73.1** & **81.6** & **77.9** & **69.5** & **88.2** & **84.9** & - & - & - \\ SCNN & - & - & - & - & - & - & 26.9 & 37.9 & - \\ HiTransformer & 66.66 & 82.11 & 63.71 & 63.90 & 86.87 & 61.55 & 31.36 & 37.25 & 29.24 \\ HiTransformer-s & 67.88 & **82.18** & 68.78 & 65.43 & 86.92 & 63.03 & **33.04** & **37.98** & **32.67** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testing results on Friend, EmotionPush, and EmoryNLP.