# GERP: A Personality-Based Emotional Response Generation Model
## 1 Introduction  
With the development of deep learning techniques, the performances of dialogue systems have been much improved. They can generate not only fluent, but also various replies based on users' queries. They respond to the user's questions anytime and anywhere, so that the user can obtain a greater sense of satisfaction and security. Early response generation algorithms obtained satisfactory results in semantic consistency and content richness. However, how to improve users' interests and generate human-computer resonance have not attracted much attention. Emotions can be used to improve the interactivity of the dialogue system and make the dialogue system more anthropomorphic. Therefore, it is essential for the chatbot to emotionally communicate with people [1; 2]. Many attempts have been made to effectively embed emotions into the generated responses of the chatbot. There are three typical ways to integrate the emotion into the response generation process. In the first way, an emotion-related energy term is used to control the emotion expression. For example, Affect-LM [3] embeds emotions into the response by adding an emotion-related energy term to an LSTM-based model. In the second way, the emotion category embedding is used to represent emotions. [PERSON] et al. [4] established a model that maps five emotion categories into low-dimensional, dense, real-valued vector representations, which directly guide the selection of contextual information and transfer emotional tendencies from source sequences to user-specific sequences. ECM [5] also uses the emotion category embeddings to compute emotion states. The emotion state decays with the decoding process in the internal memory module. The emotion will be fully expressed when the emotion state decays to 0. ECM also selects words from a sentiment vocabulary or a general vocabulary to generate responses in an external memory module. The last way is to map the emotion words into embeddings. The model built by [PERSON] et al. [6] uses emotional word embeddings to incorporate emotions intothe LSTM encoder and a standard cross-entropy loss function to learn how to generate emotionally expressive responses. The beam search is also used for diverse decoding. Deep learning techniques have been widely adopted by the dialogue systems to better express emotions. Using the adversarial learning technology, the generator is in charge of generating emotional responses given a dialogue history and a sentiment label. The adversarial discriminator enforces emotional response quality by trying to determine whether the item (dialogue history and response) comes from the real data distribution [7]. [PERSON] and [PERSON] [8] proposed the CDL framework that uses reinforcement learning to alternately generate emotional responses and emotional queries. The model consists of two ECMs with independent parameters and a sentiment classifier, which jointly solve a dual problem. The two models are alternately trained by reinforcement learning to generate content-coherent and emotional responses. In order to generate smoother and richer emotional responses, other features such as the dialog topic are introduced into the dialogue systems. TE-ECG [9] uses the topic module to obtain the topics of the conversation, so as to ensure the coherence of conversational interaction and high-quality responses. Meanwhile, the dynamic emotional attention mechanism incorporates emotions into the responses. ESCBA [10] is a syntactically constrained bidirectional asynchronous method, where pre-generated sentiment keywords and topic keywords asynchronously participant in the decoding process. However, most of these studies focused on generating emotional responses based on specified and unchanged emotions. In fact, in daily communication, the personality of the speaker plays an important role in the emotion expression. Generally speaking, there are two ways to define the personality of the dialogue system: the implicit way and the explicit way.