Building Human-Like Conversational Agents Through Commonsense-Driven Emotional Reasoning

Chatbots are based on predefined answers and rules, making it difficult to truly understand the user's emotions and achieve the same contextual capabilities as humans. Therefore, it has become a big challenge for chatbots to recognize the narrator's emotions and respond with empathetic sentences matching the topic. This research proposes an emotion classifier model to make the generated response sentences more empathetic and increase the accuracy of emotional prediction. To solve this problem, we propose a pre-trained model based on word vectors to re-train historical situation sentences, thereby improving the fluency of response sentences. The research results show that the CER model proposed in this study is better than other baseline models, reaching 41.31 % in the prediction of emotion classification. In addition to generating more empathetic response sentences, it also makes the generated response sentences more fluent. The CER model's response generation performance is closer to human response behavior, which relies on COMET to extract common-sense knowledge in each situation to help generate empathetic responses.