Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context

Siqi Shen, Charles Welch, Rada Mihalcea, Veronica Perez-Rosas

Abstract: In this paper, we introduce a counseling dialogue system that provides real-time assistance to counseling trainees. The system generates sample counselors’ reflections – i.e., responses that reflect back on what the client has said given the dialogue history. We build our model upon the recent generative pretrained transformer architecture and leverage context augmentation techniques inspired by traditional strategies used during counselor training to further enhance its performance. We show that the system incorporating these strategies outperforms the baseline models on the reflection generation task on multiple metrics. To confirm our findings, we present a human evaluation study that shows that the output of the enhanced system obtains higher ratings and is on par with human responses in terms of stylistic and grammatical correctness, as well as context-awareness.