It may be difficult for some individuals to open up and share their thoughts and feelings in front of a mental health expert. For those who are more at ease with a virtual agent, conversational agents can serve as an intermediate step in the right direction. The conversational agent must therefore be empathetic and able to conduct free-flowing conversations. To this effect, we present an approach for creating a generative empathetic open-domain chatbot that can be used for mental health applications. We leverage large scale pre-training and empathetic conversational data to make the responses more empathetic in nature and a multi-turn dialogue arrangement to maintain context. Our models achieve state-of-the-art results on the Empathetic Dialogues test set.
@article{arxiv.2111.08545,
title = {Coral: An Approach for Conversational Agents in Mental Health Applications},
author = {Harsh Sakhrani and Saloni Parekh and Shubham Mahajan},
journal= {arXiv preprint arXiv:2111.08545},
year = {2021}
}
Comments
Accepted at the 5th Workshop on Widening Natural Language Processing at the Empirical Methods in Natural Language Processing Conference (EMNLP-WiNLP), 2021