  Emotional language generation is one of the keys to human-like artificial
intelligence. Humans use different type of emotions depending on the situation
of the conversation. Emotions also play an important role in mediating the
engagement level with conversational partners. However, current conversational
agents do not effectively account for emotional content in the language
generation process. To address this problem, we develop a language modeling
approach that generates affective content when the dialogue is situated in a
given context. We use the recently released Empathetic-Dialogues corpus to
build our models. Through detailed experiments, we find that our approach
outperforms the state-of-the-art method on the perplexity metric by about 5
points and achieves a higher BLEU metric score.
