I Know the Feeling: Learning to Converse with Empathy
Year: 2018
Type: preprint
Abstract: Beyond understanding what is being discussed, human communication requires anawareness of what someone is feeling. One challenge for dialogue agents is
being able to recognize feelings in the conversation partner and reply
accordingly, a key communicative skill that is trivial for humans. Research in
this area is made difficult by the paucity of large-scale publicly available
datasets both for emotion and relevant dialogues. This work proposes a new task
for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k
conversations grounded in emotional contexts to facilitate training and
evaluating dialogue systems. Our experiments indicate that models explicitly
leveraging emotion predictions from previous utterances are perceived to be
more empathetic by human evaluators, while improving on other metrics as well
(e.g. perceived relevance of responses, BLEU scores).
Source: arXiv (Cornell University)
Authors: Hannah Rashkin, Eric M. Smith, Margaret Li, Y-Lan Boureau
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2899223584