A Unified Framework for Emotion Identification and Generation in Dialogues
This work addresses the need for more emotionally intelligent conversational agents to improve user interaction, though it is incremental in nature.
The paper tackles the problem of enhancing social chatbots by jointly identifying emotions in dialogues and generating emotionally appropriate responses, achieving superior performance over state-of-the-art models.
Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To further develop and promote social chatbots, we need to concentrate on increasing user interaction and take into account both the intellectual and emotional quotient in the conversational agents. In this paper, we propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. We employ a BERT based network for creating an empathetic system and use a mixed objective function that trains the end-to-end network with both the classification and generation loss. Experimental results show that our proposed framework outperforms current state-of-the-art models