Towards a sentiment-aware conversational agent

We propose an end-to-end sentiment-aware conversational agent based on two models: a reply sentiment prediction model and a text generation model, conditioned on the predicted sentiment and the context of the dialogue. Additionally, we propose to use a sentiment classification model to evaluate the sentiment expressed by the agent during the development of the model. Results show that explicitly guiding the text generation model with a pre-defined set of sentiment sentences leads to clear improvements, regarding the expressed sentiment and the quality of the generated text.