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Acknowledgment of Emotional States: Generating Validating Responses for Empathetic Dialogue

Zi Haur Pang, Yahui Fu, Divesh Lala, Keiko Ochi, Koji Inoue, Tatsuya Kawahara

TL;DR

The paper addresses empathetic dialogue in AI by introducing a three-module framework: validation timing detection, users' emotional state identification, and validating response generation. It leverages Task Adaptive Pre-Training with JDialogueBERT to achieve strong performance on both textual (Japanese EmpatheticDialogues) and spoken (TESC) data, outperforming baselines and ChatGPT in key metrics. The approach yields high macro-F1 and targeted emotion performance for validation timing and emotion classification, and a rule-based, evaluable validating-response generator that achieves competitive BERT scores and favorable human judgments. This framework advances empathetic human-AI interaction with validated, context-aware responses suitable for real-world dialogue systems and potential robotic applications.

Abstract

In the realm of human-AI dialogue, the facilitation of empathetic responses is important. Validation is one of the key communication techniques in psychology, which entails recognizing, understanding, and acknowledging others' emotional states, thoughts, and actions. This study introduces the first framework designed to engender empathetic dialogue with validating responses. Our approach incorporates a tripartite module system: 1) validation timing detection, 2) users' emotional state identification, and 3) validating response generation. Utilizing Japanese EmpatheticDialogues dataset - a textual-based dialogue dataset consisting of 8 emotional categories from Plutchik's wheel of emotions - the Task Adaptive Pre-Training (TAPT) BERT-based model outperforms both random baseline and the ChatGPT performance, in term of F1-score, in all modules. Further validation of our model's efficacy is confirmed in its application to the TUT Emotional Storytelling Corpus (TESC), a speech-based dialogue dataset, by surpassing both random baseline and the ChatGPT. This consistent performance across both textual and speech-based dialogues underscores the effectiveness of our framework in fostering empathetic human-AI communication.

Acknowledgment of Emotional States: Generating Validating Responses for Empathetic Dialogue

TL;DR

The paper addresses empathetic dialogue in AI by introducing a three-module framework: validation timing detection, users' emotional state identification, and validating response generation. It leverages Task Adaptive Pre-Training with JDialogueBERT to achieve strong performance on both textual (Japanese EmpatheticDialogues) and spoken (TESC) data, outperforming baselines and ChatGPT in key metrics. The approach yields high macro-F1 and targeted emotion performance for validation timing and emotion classification, and a rule-based, evaluable validating-response generator that achieves competitive BERT scores and favorable human judgments. This framework advances empathetic human-AI interaction with validated, context-aware responses suitable for real-world dialogue systems and potential robotic applications.

Abstract

In the realm of human-AI dialogue, the facilitation of empathetic responses is important. Validation is one of the key communication techniques in psychology, which entails recognizing, understanding, and acknowledging others' emotional states, thoughts, and actions. This study introduces the first framework designed to engender empathetic dialogue with validating responses. Our approach incorporates a tripartite module system: 1) validation timing detection, 2) users' emotional state identification, and 3) validating response generation. Utilizing Japanese EmpatheticDialogues dataset - a textual-based dialogue dataset consisting of 8 emotional categories from Plutchik's wheel of emotions - the Task Adaptive Pre-Training (TAPT) BERT-based model outperforms both random baseline and the ChatGPT performance, in term of F1-score, in all modules. Further validation of our model's efficacy is confirmed in its application to the TUT Emotional Storytelling Corpus (TESC), a speech-based dialogue dataset, by surpassing both random baseline and the ChatGPT. This consistent performance across both textual and speech-based dialogues underscores the effectiveness of our framework in fostering empathetic human-AI communication.
Paper Structure (19 sections, 1 equation, 2 figures, 13 tables)

This paper contains 19 sections, 1 equation, 2 figures, 13 tables.

Table of Contents

  1. Introduction
  2. Dialogue Dataset
  3. Japanese EmpatheticDialogues Dataset
  4. TUT Emotional Storytelling Corpus (TESC)
  5. Validation Timing Detection
  6. Annotation of Validation
  7. Validation Timing Detection Model
  8. Validation Timing Detection Result
  9. Users' Emotional States Identification
  10. Emotion Classification
  11. Emotion Cause Extraction
  12. Emotion Cause Annotation
  13. Emotion Cause Extraction Model
  14. Emotion Cause Extraction Result
  15. Validating Response Generation
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Examples of dialogues with validating response and non-validating response.
  • Figure 2: Overall architecture of validating response generation system