  For a computer to naturally interact with a human, it needs to be human-like.
In this paper, we propose a neural response generation model with multi-task
learning of generation and classification, focusing on emotion. Our model based
on BART (Lewis et al., 2020), a pre-trained transformer encoder-decoder model,
is trained to generate responses and recognize emotions simultaneously.
Furthermore, we weight the losses for the tasks to control the update of
parameters. Automatic evaluations and crowdsourced manual evaluations show that
the proposed model makes generated responses more emotionally aware.
