Persona aware Response Generation with Emotions

Conversational systems are the perfect examples of human-machine interactions. The conversational agents while interacting with humans lack the ability to express emotions and behave inconsistently, making the conversations boring and non-interactive. In this work, we propose the task of persona aware emotional response generation in which the system can generate specific and consistent responses in accordance to the provided personality information and the conversational history. To make the responses interactive and interesting we intend to infuse the emotions in the responses that help in making the responses more human-like. We propose a persona aware attention framework employing an encoder-decoder approach. We investigate different ways to include the desired emotions in the responses. Experimental results on the PersonaChat dataset shows that our proposed framework outperforms the baseline models and can generate interactive and emotional responses.