Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities

TL;DR

This review analyzes recent advances in empathetic conversational systems, highlighting dominant datasets, current methodologies, and identifying key gaps and future research opportunities in emotion detection, multimodal inputs, and nuanced empathy modeling.

Contribution

The paper provides a comprehensive review of current empathetic conversational systems, identifying research gaps and proposing future directions for enhancing empathy in dialogue models.

Findings

Most studies use the EMPATHETICDIALOGUES dataset

Text-based modality dominates research in this field

Incorporating emotion causes and external knowledge improves response quality

Abstract

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.