# GERP: A Personality-Based Emotional Response Generation Model
## 1 Introduction
The implicit methods usually extract the personality from the dialogue history. However, the explicit methods directly define the personality of the dialogue system using documents, vectors, or codes in advance. Implicit methods use datasets that are readily available to extract the embedded personalities. For example, [PERSON] et al. [11] designed a personalized language model to construct a generic user portrait from their historical replies. The work introduced a key-value pair memory neural network to store the user's historical input-reply pairs and generate the dynamic user portrait. The dynamic user portrait mainly reflects what and how the user responds to similar inputs in the history. However, the portrait cannot be directly obtained. Therefore, it is inconvenient for others to observe and modify the extracted personality. As a comparison, the explicit methods describe the personality of the dialog system in a more direct way, which can be documents or embeddings, etc. Inspired by word embedding, the personality can be expressed as a persona embedding, which can be added into the encoding process to generate responses. [PERSON] et al. [12] proposed a model based on the personality embedding. It was the first one to introduce personality into the chatbot model. In addition to the persona embedding, the profile is also commonly used to describe the chatbot personality. [PERSON] et al. [13] used the key-value profile to define the personality of the chatbot, such as: \"Name: [PERSON]; Age: 18; Gender: Male; Hobbies: Anime; Specially: Piano\". According to the user's input, a certain keyword in the profile is selected, and then, a reply is generated based on this word. Another common form to describe the personality is the sentence profile. [PERSON] et al. [14] described the chatbot personality using a five-sentence profile. A profile in the sentence format can be: \"I like to ski. My wife does not like me anymore. I have went to Mexico 4 times this year. I hate Mexican food. I like to eat cheetos.\" In a memory-enhanced neural network, it is used to produce responses that are more personal, specific, consistent, and attractive than a role-free model. However, a dataset that meets the above requirements needs to be constructed at an expensive cost [13]. At the same time, these profiles focus on the personal information of the chatbot, such as the name, gender, age, and hobbies, which help address the consistency of the input and responses. Unfortunately, in these works, they are independent of the emotions to be generated in the responses. For the above reasons, our work employed an explicit method that offers a balance between simplicity and ease of emotional expression: the \"Big Five\" personality vectors (which are described in Section 2.1.1) to describe the personality of the robot. The \"Big Five\" personality vectors have been employed in chatbots. For example, some studies incorporated the \"Big Five\" personality traits directly into the decoding process of LSTM [15] or classified chatbots based on the personality [16]. [PERSON] et al. [17] combined the personality with the emotion, but they only considered a single factor in the \"Big Five\" personality traits. To sum up, existing studies primarily focus on directly and separately integrating the \"Big Five\" personality vector into the design of chatbots without fully integrating it with other characteristics, such as emotions. As discussed above, very few works have fully considered the impact of the chatbot's personality on the emotional response generation. How to generate suitable emotional responses for the chatbot based on its personality is a problem worthy of exploration. In this work, we propose a new model, namely Generating Emotional Responses based on the chatbot's Personality (GERP), which generates emotional responses based on a pre-defined personality of the chatbot in the form of the \"Big Five\" personality vectors (which are described in Section 2.1.1). The responses generated by GERP make the chatbot more anthropomorphic.