Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation

Zhou Yang, Zhaochun Ren, Yufeng Wang, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Yunbing Wu, Yisong Su, Sibo Ju, Xiangwen Liao

TL;DR

This work tackles the challenge of empathetic response generation by modeling dynamic emotion–semantic correlations in dialogue. It introduces ESCM, which builds dynamic emotion–semantic vectors and uses a dependency-tree guided dynamic correlation graph convolutional network to learn context meaning and generate empathetic responses. Experiments on the EMPATHETIC-DIALOGUES dataset show ESCM improves emotion perception, response diversity, and fluency, with analyses confirming the frequent and linguistically consistent use of emotion–semantics correlations in dialogue. The approach offers practical benefits for more nuanced and informative empathetic systems, and suggests avenues for incorporating pre-trained models, multilinguality, and personalization in future work.

Abstract

Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with semantic meanings, in grammar. Previous methods overlook these two characteristics, which easily lead to misunderstandings of emotions and neglect of key semantics. To address this issue, we propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion-semantic vectors through the interaction of context and emotions. We introduce dependency trees to reflect the correlations between emotions and semantics. Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM understands semantics and emotions more accurately and expresses fluent and informative empathetic responses. Our analysis results also indicate that the correlations between emotions and semantics are frequently used in dialogues, which is of great significance for empathetic perception and expression.

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation

TL;DR

This work tackles the challenge of empathetic response generation by modeling dynamic emotion–semantic correlations in dialogue. It introduces ESCM, which builds dynamic emotion–semantic vectors and uses a dependency-tree guided dynamic correlation graph convolutional network to learn context meaning and generate empathetic responses. Experiments on the EMPATHETIC-DIALOGUES dataset show ESCM improves emotion perception, response diversity, and fluency, with analyses confirming the frequent and linguistically consistent use of emotion–semantics correlations in dialogue. The approach offers practical benefits for more nuanced and informative empathetic systems, and suggests avenues for incorporating pre-trained models, multilinguality, and personalization in future work.

Abstract

Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with semantic meanings, in grammar. Previous methods overlook these two characteristics, which easily lead to misunderstandings of emotions and neglect of key semantics. To address this issue, we propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion-semantic vectors through the interaction of context and emotions. We introduce dependency trees to reflect the correlations between emotions and semantics. Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM understands semantics and emotions more accurately and expresses fluent and informative empathetic responses. Our analysis results also indicate that the correlations between emotions and semantics are frequently used in dialogues, which is of great significance for empathetic perception and expression.
Paper Structure (20 sections, 13 equations, 3 figures, 8 tables)

This paper contains 20 sections, 13 equations, 3 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Task Formulation
  5. Overview
  6. Context Encoder
  7. Dynamic Correlation Encoding Module
  8. Emotion and Response Predicting
  9. Experiments
  10. Baselines
  11. Implementation Details
  12. Evaluation Metrics
  13. Results and Analysis
  14. Main Results
  15. Ablation Studies
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Examples from the EMPATHETIC-DIALOGUES dataset. Sentence 1 shows the variability of emotional words. Sentence 2 shows the correlations of emotional words with semantic roles.
  • Figure 2: An overview of ESCM. ESCM consists of three main key modules: (1) a context encoder (Section \ref{['Context Encoder']}), which encodes the semantic of context. (2) a dynamic correlation encoding module (Section \ref{['Dynamic Correlation Encoding Module']}), which learns the correlations between emotions and semantics. (3) emotion and response predicting module (Section \ref{['Emotion and Response Predicting']}), which predicts dialog emotion categories and generates empathetic responses.
  • Figure 3: The percentage of frequently used correlations to the total number of correlations.