A survey on empathetic dialogue systems
By Yukun Ma, K. L. Nguyen, Frank Xing, Erik Cambria. 2020
237 citations recorded by the source corpus.
Abstract Dialogue systems have achieved growing success in many areas thanks to the rapid advances of machine learning techniques. In the quest for generating more human-like conversations, one of the major challenges is to learn to generate responses in a more empathetic manner. In this review article, we focus on the literature of empathetic dialogue systems, whose goal is to enhance the perception and expression of emotional states, personal preference, and knowledge. Accordingly, we identify three key features that underpin such systems: emotion-awareness, personality-awareness, and knowledge-accessibility. The main goal of this review is to serve as a comprehensive guide to research and development on empathetic dialogue systems and to suggest future directions in this domain.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Soujanya Poria
- Erik Cambria
- Devamanyu Hazarika
- Navonil Majumder
- Rada Mihalcea
- Louis-philippe Morency
- Percy Liang
- Tom Young
Citations in the corpus
- Understanding and predicting user dissatisfaction in a neural generative chatbot (2021, background): “Empathy is a fundamental part of human communication, and can improve user experience of dialogue agents (Ma et al., 2020).”
- Can Large Language Models Transform Computational Social Science? (2023, background): “…triggers for empathetic responses (Buechel et al., 2018; Omitaomu et al., 2022), CSS can reciprocally inform the design of empathetic communities (Coulton et al., 2014; Taylor et al., 2019), as well as community-specific tools like counseling dialogue systems (Sharma et al., 2021; Ma et al., 2020).”
- A Metaverse: Taxonomy, Components, Applications, and Open Challenges (2022, background): “For the dialogue system to sustain longer and more human conversations, empathic dialogue systems consider personas [94], [95].”
- Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “ctive content that can emulate human communication. Such abilities are desirable in many applications such as comment/review generation (Dong et al., 2017), and emotional chatbots (Zhou et al., 2018; Ma et al., 2020). Early efforts in this direction included works that either focused on related topi”
- Recent advances in deep learning based dialogue systems: a systematic survey (2021, background): “Some works (Liu et al., 2019; Mehri et al., 2019; Chen et al., 2019c; Ma et al., 2020a) combined RNNs as a part of dialogue representation models to train dialogue embeddings and further improved the performance of dialogue-related tasks.”