## 4 Results
### Results and Analysis
#### 4.3.2 Comparative Analysis of Experimental Results  
In order to further verify the effectiveness of our proposed model, we compared it with related models at the forefront of research and selected four leading-edge emotional dialogue generating models that had achieved good results as baseline models. The baseline models included the following:  
The Seq2 Seq model [14]: This model proposes a general end-to-end sequence-learning method. In this method, the encoder maps the input sequence to a fixed-dimensional vector. The fixed-dimensional vector is regarded as the semantic expression of the input sequence. The decoder generates the output sequence according to the semantic expression of the input sequence. The ECM (Emotional Chatting Machine) model [20]: Based on the traditional sequence-to-sequence model with static emotion vector expression, a dynamic emotional state memory network, and an external memory mechanism of emotional words, the ECM can output responses that correspond to the emotions defined by the designated emotion categories. The EmoEmb (emotion embedding dialogue system) model [15]: This method represents each emotion category as a vector based on the Seq2 Seq framework, and inputs them into the decoder at each time step. The EmoDS (emotional dialogue system) model [21]: This model seamlessly \"inserts\" text by increasing the probability of emotional vocabularies at the correct time step, while guiding the response generation process through an emotion classifier to ensure that the generated text contains the specific emotions. As shown in the comparison results of the baseline model experiments in Table 4, the DESG model outperformed other models in terms of the BLEU evaluation index, emotional performance, and response diversity; and was superior to the Seq2 Seq, EmoEmb, and ECM models as an evaluation method based on word vectors, although it was not as good as the EmoDS model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Baseline** \\ **Models** \\ \end{tabular} } & \multirow{2}{*}{**BLEU**} & \multirow{2}{*}{**Word Vector Based Method**} & \multicolumn{2}{c}{**Emotional**} & \multirow{2}{*}{**Responses Diversity**} \\ \cline{2-2} \cline{5-8}  & & & & & & & **Performance** & **Responses Diversity** \\ \cline{2-2} \cline{5-8}  & **BLEU** & **Average** & **Greedy** & **Extreme** & **Emotion-a** & **Emotion-w** & **Distinct-1** & **Distinct-2** \\ \hline Seq2 Seq & 1.50 & 0.523 & 0.376 & 0.350 & 0.355 & 0.371 & 0.0038 & 0.012 \\ EmoEmb & 1.69 & 0.524 & 0.381 & 0.355 & 0.720 & 0.512 & 0.0054 & 0.0484 \\ ECM & 1.68 & 0.624 & 0.434 & 0.409 & 0.765 & 0.580 & 0.0090 & 0.0735 \\ EmoDS & 1.73 & 0.634 & 0.451 & 0.435 & 0.810 & 0.687 & 0.0113 & 0.0867 \\ \hline DESG & 1.74 & 0.632 & 0.450 & 0.433 & 0.813 & 0.690 & 0.0121 & 0.0869 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons with baselines. In terms of the BLEU evaluation index and the word-vector-based method, our model DESG improved significantly on the baseline model Seq2 Seq, with improvements of 0.24, 0.11, 0.074, and 0.084 corresponding to BLEU, Average, Greedy, and Extreme, respectively. This indicates that the DESG model was able produce appropriate output sequences in terms of content (related grammar). In respect to the emotional performance and response diversity indicators, the baseline Seq2 Seq model performed quite poorly, as this model only generates short general responses and does not consider any emotional factors. In terms of emotional performance and response diversity, the DESG model improved significantly on to the Seq2 Seq model, with improvements of 0.458, 0.319, 0.0083, and 0.0749 corresponding to Emotion-a, Emotion-w, Distinct-1, and Distinct-2, respectively. This indicates that our model was not only able to generate appropriate output sequences in terms of content (related grammar), but could also explicitly or implicitly express desired emotional responses.