# CAiRE: An End-to-End Empathetic Chatbot

**Authors:** Zhaojiang Lin, Peng Xu, Genta Indra Winata, Zihan Liu, Pascale Fung  
**arXiv:** [1907.12108](https://arxiv.org/abs/1907.12108)  
**Date:** 2019-07-28  
**Summary page:** https://www.summarizepaper.com/fr/paper/1907.12108v1/

## Abstract

In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We evaluate our model on the recently proposed empathetic-dialogues dataset (Rashkin et al., 2019), the experiment results show that CAiRE achieves state-of-the-art performance on dialogue emotion detection and empathetic response generation.