  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.
