I Enjoy Writing and Playing, Do You?: A Personalized and Emotion Grounded Dialogue Agent Using Generative Adversarial Network

Social chatbots have gained immense popularity, and their appeal lies in their capacity to respond to diverse requests, but also in their ability to develop an emotional connection with users. To develop and promote social chatbots, we need to concentrate on increasing user interaction and consider both the intellectual and emotional quotient in conversational agents. In this work, we propose the task of empathetic, personalized dialogue generation giving the machine the capability to respond emotionally and in accordance with the persona of the user. We design a generative adversarial framework, named EP-GAN (Empathy and Persona aware Generative Adversarial Network) to generate responses that are sensitive to the emotion of the user and corresponds to the persona. The persona information is encoded as memory vectors that, along with the dialogue history, are fed to the decoder for generation. An interactive adversarial learning framework is implemented to verify whether the generated responses elicit the emotion and persona in dialogues. Experimental results show that the EP-GAN framework significantly outperforms the existing baselines for both automatic and manual evaluation. We achieve an improvement of 1 point BLEU-4 and 2 points in the emotion accuracy metric compared to the best performing baseline.