  One challenge for dialogue agents is recognizing feelings in the conversation
partner and replying accordingly, a key communicative skill. While it is
straightforward for humans to recognize and acknowledge others' feelings in a
conversation, this is a significant challenge for AI systems due to the paucity
of suitable publicly-available datasets for training and evaluation. This work
proposes a new benchmark for empathetic dialogue generation and
EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional
situations. Our experiments indicate that dialogue models that use our dataset
are perceived to be more empathetic by human evaluators, compared to models
merely trained on large-scale Internet conversation data. We also present
empirical comparisons of dialogue model adaptations for empathetic responding,
leveraging existing models or datasets without requiring lengthy re-training of
the full model.
