CAiRE: An End-to-End Empathetic Chatbot

07/28/2019
by   Zhaojiang Lin, et al.
0

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.

READ FULL TEXT
research
11/29/2022

End-to-End Neural Discourse Deixis Resolution in Dialogue

We adapt Lee et al.'s (2018) span-based entity coreference model to the ...
research
09/06/2018

Training Millions of Personalized Dialogue Agents

Current dialogue systems are not very engaging for users, especially whe...
research
04/19/2021

When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting

Since the seminal work of Mikolov et al. (2013a) and Bojanowski et al. (...
research
12/04/2018

Practical Text Classification With Large Pre-Trained Language Models

Multi-emotion sentiment classification is a natural language processing ...
research
05/27/2021

Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT

Retrieval-based dialogue systems select the best response from many cand...
research
09/26/2022

Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation

The task of empathetic response generation aims to understand what feeli...
research
02/07/2018

Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus

Ubuntu dialogue corpus is the largest public available dialogue corpus t...

Please sign up or login with your details

Forgot password? Click here to reset