Validating Response Generation Using Emotional Storytelling Corpus

Abstract

Validation, a key communication technique that entails recognizing, understanding, and accepting others' emotional states, thoughts, and actions, plays a vital role in fostering strong interpersonal relationships. This work introduces a pioneering system for generating validating responses to foster empathetic dialogue. Utilizing the TUT Emotional Storytelling Corpus (TESC) - a Japanese multi-turn corpus detailing 8 emotional categories from Plutchik's wheel of emotions - our Task Adaptive Pre-Training (TAPT) BERT-based approach achieves 56.5% precision and accuracy in pinpointing validating response generation moments, and 88.6% precision and accuracy in emotion classification during validating response generation. For practical evaluation, we compared our system against Seq2Seq and ChatGPT empathetic responses through human assessment.