## 2 Related Work
They connected two decoders to one encoder, simulating the manner in which different people reading the same post might have different thoughts and then generate different responses. [PERSON] et al. (2019) [19] proposed methods to generate emotionally aware responses and mimic human conversations. These models were able to determine the relevant emotions by which to reply to a user post and generate responses appropriately. [PERSON] et al. (2018) [20] proposed the ECM (Emotional Chatting Machine) model, which is a large-scale generative sequence-to-sequence model that can respond to users emotionally. Based on the traditional sequence-to-sequence model, this method uses a static emotion vector expression, a dynamic emotional state memory network, and an external memory of emotional words, so that the ECM can output responses corresponding to emotions according to the specified emotion classification. However, the ECM must specify the emotion categories to be generated, which means that an external decision-maker is required. In order to solve the problem of needing an external decision-maker for the ECM model to work, [PERSON] et al. (2019) [21] proposed the EmoDS (emotional dialogue system) model, which increases the probability of emotional vocabulary at the correct time-step to seamlessly \"insert\" text. At the same time, by guiding the response generation process through the emotion classifier, it is ensured that the generated text contains specific emotions. Inspired by psychologists, we found that emotional responses in sentences are dynamic [22]. However, the EmoDS model does not fully consider the need for dynamic emotions in sentences. [PERSON] et al. [21] found that there are at least two ways (explicit and implicit) to incorporate emotional information into a sentence. One is to describe the emotional state (such as \"anger\", \"disgust\", \"satisfaction\", \"happiness\", \"sadness\", etc.) by explicitly using strong emotional words; the other is to increase the intensity of the emotional experience, not by using words from an emotional vocabulary, but by implicitly combining neutral vocabulary in different ways to express emotions. As shown in Table 1, for each post, one emotional response is listed below. The emotional words associated with strong feelings are highlighted in a bold blue font. We propose a dynamic emotional session generation model (DESG) based on the Seq2 Seq framework and a dictionary-based attention mechanism [23]. At the same time, in order to improve the model, two mechanisms are introduced: (1) An internal emotion regulator that is able to perform high-level abstract emotional performances and capture changes in implicit emotional states via embedded emotion categories. (2) An emotion classifier, which is used to guide the response generation process and ensure that the generated output sequence contains the appropriate emotion category. The DESG not only uses a variety of sorting algorithms to improve the diversity of output sequences, but also possess a relatively \"accurate\" emotion dictionary to express human experience and perception. The experimental results show that for a given input sequence and emotion category, our DESG model can not only generate a suitable output sequence in terms of content (relevant grammar), but also express the desired emotional response explicitly or implicitly. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Post** & **Explicit** & **Implicit** \\ \hline I bought a beautiful dress & Wearing a beautiful dress makes & Wow, you must feel walking on \\ yesterday! & me happy! & air! \\ \hline The weather today is really nice. & I like sunny days. & I’m in a better mood today. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Two (explicit and implicit) varieties of emotional performance.