# Dialog CIN: Contextual Inference Networks for Emotional Dialogue Generation
## 4 Proposed Dataset
### Data Processing
On the other hand, certain dialogues may entail greater intricacy, demanding in-depth reasoning, incorporation of background knowledge, and contextual comprehension. Consequently, the diversity of utterance counts in the dialogue corpus contributes to the system's robustness and its ability to generalize effectively across different conversational scenarios. The dataset's inclusion of dialogues with varying utterance counts ensures that the model acquires the necessary adaptability to address a wide range of conversational complexities and ultimately enhances its performance in real-world applications. The emotional labels in the dataset were defined based on the \"Big Six Theory\" [31], which posits that there are six primary emotions in humans, namely, anger, disgust, fear, happiness, sadness, surprise. However, during the annotation process, it was recognized that the existing emotion labels needed to be expanded to encompass a broader spectrum of emotions. Consequently, the label 'Neutral' was introduced to account for instances \begin{table}
\begin{tabular}{c c} \hline \hline Total Dialogues & 10,045 \\ Average Utterances Per Dialogue & 7.1 \\ Average Tokens Per Dialogue & 112.7 \\ Average Tokens Per Utterance & 16.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical information on the LifeDialog dataset. Figure 4: Distribution of the number of utterances in LifeDialog. of emotional neutrality or the absence of expressed emotions. As a result, the LifeDialog dataset now comprises a comprehensive set of seven emotion labels: {Anger, Disgust, Fear, Happiness, Sadness, Surprise, Neutral}. The distribution of emotions within the LifeDialog dataset is depicted in Figure 5. The emotion label \"Happiness\" exhibited the highest count, while the counts of the \"Sadness\" and \"Anger\" labels were relatively balanced. In contrast, the counts of the \"Surprise,\" \"Disgust,\" and \"Fear\" labels were lower. These observations provide several insights: happiness is a commonly expressed emotion in everyday conversations, and individuals tend to convey negative emotions such as sadness and anger within their dialogues. Conversely, the occurrence of surprise, disgust, and fear emotions is less frequent in daily conversations, potentially indicating their association with specific contexts. Additionally, in public dialogues, individuals often exhibit neutral or mild emotions influenced by social norms. The presence of multiple emotion labels reflects the diverse range of emotional expressions and variations in the dialogue, thereby facilitating the dialogue system in better comprehending user emotions and generating accurate emotional responses. During communication, conversational behavior often reflects the underlying intentions individuals aim to achieve. In light of the various types of Chinese sentences, we categorized the sentence class labels into four distinct categories: declarative sentences, interrogative sentences, imperative sentences, and exclamatory sentences. Declarative sentences primarily convey information and describe situations, including both positive and negative scenarios. Interrogative sentences are primarily used to inquire about situations. Imperative sentences are mainly employed to make requests or suggest that others take certain actions or refrain from doing so. Exclamatory sentences can express the speaker's opinions or convey intense emotions. The distribution of sentence types within the LifeDialog dataset is illustrated in Figure 6. By examining this distribution, we gained insights into the frequency and relative importance of different sentence types in dialogues. This analysis aids in understanding the varying degrees of emphasis placed on different sentence types during conversations, shedding light on their respective roles within the dialogue context. Figure 5: Distribution of Emotion in LifeDialog. The LifeDialog dataset is an extensive collection of real-life conversations, encompassing a diverse range of daily scenarios.