Abstract
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 retraining of the full model