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

Siqi Shen, Charles Welch, Rada Mihalcea, Verónica Pérez-Rosas


Abstract
We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills. The system generates counselors’reflections – i.e., responses that reflect back on what the client has said given the dialogue history. Our method builds upon the new generative pretrained transformer architecture and enhances it with context augmentation techniques inspired by traditional strategies used during counselor training. Through a set of comparative experiments, we show that the system that incorporates these strategies performs better in the reflection generation task than a system that is just fine-tuned with counseling conversations. To confirm our findings, we present a human evaluation study that shows that our system generates naturally-looking reflections that are also stylistically and grammatically correct.
Anthology ID:
2020.sigdial-1.2
Volume:
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
July
Year:
2020
Address:
1st virtual meeting
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–20
URL:
https://www.aclweb.org/anthology/2020.sigdial-1.2
DOI:
Bib Export formats:
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PDF:
https://www.aclweb.org/anthology/2020.sigdial-1.2.pdf

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